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Journal articles on the topic 'Dynamic Graph Metrics'
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Sizemore, Ann E., and Danielle S. Bassett. "Dynamic graph metrics: Tutorial, toolbox, and tale." NeuroImage 180 (October 2018): 417–27. http://dx.doi.org/10.1016/j.neuroimage.2017.06.081.
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Davis, Jacob D., and Eberhard O. Voit. "Metrics for regulated biochemical pathway systems." Bioinformatics 35, no. 12 (November 14, 2018): 2118–24. http://dx.doi.org/10.1093/bioinformatics/bty942.
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Abstract Motivation The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems. Results The metrics permit quantitative characterizations of the importance of regulation in biochemical pathway systems, including systems designed for applications in synthetic biology or metabolic engineering. They may also become criteria for effective model reduction. Availability and implementation The source code is available at https://gitlab.com/tienbien44/metrics-bsa
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Sunarmodo, Wismu, and Bayu Distiawan Trisedya. "Anchored Self-Supervised Dynamic Graph Representation Learning for Aviation Data as A Fast Economic Indicator." International Journal on Advanced Science, Engineering and Information Technology 14, no. 6 (December 20, 2024): 1842–48. https://doi.org/10.18517/ijaseit.14.6.20170.
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The Fast Economic Growth Indicator, a newly developed metric leveraging big data, provides policymakers with timely insights crucial for assessing the economic impact of policies or events. Among various open-source datasets, aviation data stands out as a potential indicator of rapid economic growth, given its inherent graph structure with airports as nodes and flight connections as edges. However, global flight data, being dynamic and complex, poses challenges in analysis. To glean comprehensive insights, it's imperative to condense this graph data into representative vector values while preserving node relationships. In this study, we utilize the dynamic graph node embedding method to quantify the influence levels of airports relative to each other. Traditional node embedding methods often prioritize homophily over structural equivalence, challenging directly extracting influence levels. To address this limitation, we introduce anchored dynamic graph node embedding, employing a virtual node as a reference point in embedding space to enable direct calculation of influence levels. These influence metrics are then compared to the GDP of airport regions. Using USA domestic flight data from 1988 to 2021 as a case study, our methodology demonstrates promising results, boasting a 0.94 correlation coefficient with national GDP and a 0.9 correlation coefficient with state Gross State Product (GSP). This research aims to advance dynamic graph node embedding methods towards structural equivalence rather than homophily, enhancing applicability to tasks emphasizing node structure over neighborhood proximity. An example of the benefits of this research is its utility in addressing Influence Maximization Problems within dynamic graphs.
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Antal, Gábor, Zoltán Tóth, Péter Hegedűs, and Rudolf Ferenc. "Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics." Technologies 9, no. 1 (December 30, 2020): 3. http://dx.doi.org/10.3390/technologies9010003.
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Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.
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Wang, Mingjie, Yifan Huo, Junhong Zheng, and Lili He. "SC-TKGR: Temporal Knowledge Graph-Based GNN for Recommendations in Supply Chains." Electronics 14, no. 2 (January 7, 2025): 222. https://doi.org/10.3390/electronics14020222.
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Graph neural networks (GNNs) are widely used in recommendation systems to improve prediction performance, especially in scenarios with diverse behaviors and complex user interactions within supply chains. However, while existing models have achieved certain success in capturing the temporal and dynamic aspects of supply chain behaviors, challenges remain in effectively addressing the time-sensitive fluctuations of market demands and user preferences. Motivated by these challenges, we propose SC-TKGR, a supply chain recommendation framework based on temporal knowledge graphs. It employs enhanced time-sensitive graph embedding methods to model behavioral temporal characteristics, incorporates external factors to capture market dynamics, and utilizes contrastive learning to handle sparse information efficiently. Additionally, static feature knowledge graph embeddings are incorporated to complement temporal modeling by capturing complex retailer–product relationships. Experiments on real-world electrical equipment industry datasets demonstrate that SC-TKGR achieves superior performance in NDCG and Recall metrics, offering a robust approach for capturing trend-level demand shifts and market dynamics in supply chain recommendations, thereby aiding strategic planning at a monthly scale and operational adjustments.
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Ruf, Verena, Anna Horrer, Markus Berndt, Sarah Isabelle Hofer, Frank Fischer, Martin R. Fischer, Jan M. Zottmann, Jochen Kuhn, and Stefan Küchemann. "A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning." Education Sciences 13, no. 2 (February 19, 2023): 216. http://dx.doi.org/10.3390/educsci13020216.
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The interpretation of graphs plays a pivotal role in education because it is relevant for understanding and representing data and comprehending concepts in various domains. Accordingly, many studies examine students’ gaze behavior by comparing different levels of expertise when interpreting graphs. This literature review presents an overview of 32 articles comparing the gaze behavior of experts and non-experts during problem-solving and learning with graphs up to January 2022. Most studies analyzed students’ dwell time, fixation duration, and fixation count on macro- and meso-, as well as on micro-level areas of interest. Experts seemed to pay more attention to relevant parts of the graph and less to irrelevant parts of a graph, in line with the information-reduction hypothesis. Experts also made more integrative eye movements within a graph in terms of dynamic metrics. However, the determination of expertise is inconsistent. Therefore, we recommend four factors that will help to better determine expertise. This review gives an overview of evaluation strategies for different types of graphs and across various domains, which could facilitate instructing students in evaluating graphs.
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Liu, Liu, Sibren Isaacman, and Ulrich Kremer. "An Adaptive Application Framework with Customizable Quality Metrics." ACM Transactions on Design Automation of Electronic Systems 27, no. 2 (March 31, 2022): 1–33. http://dx.doi.org/10.1145/3477428.
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Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.
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Yu, Mingqin, Fethi A. Rabhi, and Madhushi Bandara. "Ontology-Driven Architecture for Managing Environmental, Social, and Governance Metrics." Electronics 13, no. 9 (April 29, 2024): 1719. http://dx.doi.org/10.3390/electronics13091719.
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The burgeoning significance of environmental, social, and governance (ESG) metrics in realms such as investment decision making, corporate reporting, and risk management underscores the imperative for a robust, comprehensive solution capable of effectively capturing, representing, and analysing the multifaceted and intricate ESG data landscape. Facing the challenge of aligning with diverse standards and utilising complex datasets, organisations require robust systems for the integration of ESG metrics with traditional financial reporting. Amidst this, the evolving regulatory landscape and the demand for transparency and stakeholder engagement present significant challenges, given the lack of standardized ESG metrics in certain areas. Recently, the use of ontology-driven architectures has gained attention for their ability to encapsulate domain knowledge and facilitate integration with decision-support systems. This paper proposes a knowledge graph in the ESG metric domain to assist corporations in cataloguing and navigating ESG reporting requirements, standards, and associated data. Employing a design science methodology, we developed an ontology that serves as both a conceptual foundation and a semantic layer, fostering the creation of an interoperable ESG Metrics Knowledge Graph (ESGMKG) and its integration within operational layers. This ontology-driven approach promises seamless integration with diverse ESG data sources and reporting frameworks, while addressing the critical challenges of metric selection, alignment, and data verification, supporting the dynamic nature of ESG metrics. The utility and effectiveness of the proposed ontology were demonstrated through a case study centred on the International Financial Reporting Standards (IFRS) framework that is widely used within the banking industry.
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Chen, Yuhang, Jiaxin Jiang, Shixuan Sun, Bingsheng He, and Min Chen. "RUSH: Real-Time Burst Subgraph Detection in Dynamic Graphs." Proceedings of the VLDB Endowment 17, no. 11 (July 2024): 3657–65. http://dx.doi.org/10.14778/3681954.3682028.
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Graph analytics have been effective in the data science pipeline of fraud detections. In the ever-evolving landscape of e-commerce platforms like Grab or transaction networks such as cryptos, we have witnessed the phenomenon of 'burst subgraphs,' characterized by rapid increases in subgraph density within short timeframes---as a common pattern for fraud detections on dynamic graphs. However, existing graph processing frameworks struggle to efficiently manage these due to their inability to handle sudden surges in data. In this paper, we propose RUSH ( R eal-time b U rst S ubgrap H detection framework), a pioneering framework tailored for real-time fraud detection within dynamic graphs. By focusing on both the density and the rate of change of subgraphs, RUSH identifies crucial indicators of fraud. Utilizing a sophisticated incremental update mechanism, RUSH processes burst subgraphs on large-scale graphs with high efficiency. Furthermore, RUSH is designed with user-friendly APIs that simplify the customization and integration of specific fraud detection metrics. In the deployment within Grab's operations, detecting burst subgraphs can be achieved with approximately ten lines of code. Through extensive evaluations on real-world datasets, we show RUSH's effectiveness in fraud detection and its robust scalability across various data sizes. In case studies, we illustrate how RUSH can detect fraud communities within various Grab business scenarios, such as customer-merchant collusion and promotion abuse, and identify wash trading in crypto networks.
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Mu, Bo, Guohang Tian, Gengyu Xin, Miao Hu, Panpan Yang, Yiwen Wang, Hao Xie, Audrey L. Mayer, and Yali Zhang. "Measuring Dynamic Changes in the Spatial Pattern and Connectivity of Surface Waters Based on Landscape and Graph Metrics: A Case Study of Henan Province in Central China." Land 10, no. 5 (May 1, 2021): 471. http://dx.doi.org/10.3390/land10050471.
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An understanding of the scientific layout of surface water space is crucial for the sustainable development of human society and the ecological environment. The objective of this study was to use land-use/land-cover data to identify the spatiotemporal dynamic change processes and the influencing factors over the past three decades in Henan Province, central China. Multidisciplinary theories (landscape ecology and graph theory) and methods (GIS spatial analysis and SPSS correlation analysis) were used to quantify the dynamic changes in surface water pattern and connectivity. Our results revealed that the water area decreased significantly during the periods of 1990–2000 and 2010–2018 due to a decrease in tidal flats and linear waters, but increased significantly in 2000–2010 due to an increase in patchy waters. Human construction activities, socioeconomic development and topography were the key factors driving the dynamics of water pattern and connectivity. The use of graph metrics (node degree, betweenness centrality, and delta probability of connectivity) in combination with landscape metrics (Euclidean nearest-neighbor distance) can help establish the parameters of threshold distance between connected habitats, identify hubs and stepping stones, and determine the relatively important water patches that require priority protection or development.
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Catal, Cagatay, Hakan Gunduz, and Alper Ozcan. "Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems." Electronics 10, no. 20 (October 18, 2021): 2534. http://dx.doi.org/10.3390/electronics10202534.
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Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that damage the infrastructure of the entire ITS. Attackers can inject malware code that triggers dangerous actions such as information theft and unwanted system moves. The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks. To detect malware attacks addressing ITS, a Graph Attention Network (GAN)-based framework is proposed in this study. The inputs to this framework are the Application Programming Interface (API)-call graphs obtained from malware and benign Android apk files. During the graph creation, network metrics and the Node2Vec model are utilized to generate the node features. A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN). Experimental results demonstrated that the integration of the GAN and Node2Vec models provides the best performance in terms of F-measure and accuracy parameters and, also, the use of an attention mechanism in GAN improves the performance. Furthermore, node features generated with Node2Vec resulted in a 3% increase in classification accuracy compared to the features generated with network metrics.
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Iglesias-Parro, Sergio, María F. Soriano, Antonio J. Ibáñez-Molina, Ana V. Pérez-Matres, and Juan Ruiz de Miras. "Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach." Sensors 23, no. 21 (October 25, 2023): 8722. http://dx.doi.org/10.3390/s23218722.
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Schizophrenia (SZ) is a complex disorder characterized by a range of symptoms and behaviors that have significant consequences for individuals, families, and society in general. Electroencephalography (EEG) is a valuable tool for understanding the neural dynamics and functional abnormalities associated with schizophrenia. Research studies utilizing EEG have identified specific patterns of brain activity in individuals diagnosed with schizophrenia that may reflect disturbances in neural synchronization and information processing in cortical circuits. Considering the temporal dynamics of functional connectivity provides a more comprehensive understanding of brain networks’ organization and how they change during different cognitive states. This temporal perspective would enhance our understanding of the underlying mechanisms of schizophrenia. In the present study, we will use measures based on graph theory to obtain dynamic and static indicators in order to evaluate differences in the functional connectivity of individuals diagnosed with SZ and healthy controls using an ecologically valid task. At the static level, patients showed alterations in their ability to segregate information, particularly in the default mode network (DMN). As for dynamic measures, patients showed reduced values in most metrics (segregation, integration, centrality, and resilience), reflecting a reduced number of dynamic states of brain networks. Our results show the utility of combining static and dynamic indicators of functional connectivity from EEG sensors.
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Louca, L. S., and J. L. Stein. "Ideal physical element representation from reduced bond graphs." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 216, no. 1 (February 1, 2002): 73–83. http://dx.doi.org/10.1243/0959651021541444.
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Previous research has demonstrated that bond graphs are a natural and convenient representation to implement energy-based metrics that evaluate the relative ‘value’ of energy elements in a dynamic system model. Bond graphs also provide a framework for systematically reformulating a reduced bond graph model (and thus the state equations) of the system that results from eliminating the ‘unimportant’ elements. This paper shows that bond graphs also provide a natural and convenient representation for developing a rigorous approach for interpreting the removal of ideal energy elements from the system model. For example, when a generalized inductance in the mechanical domain is eliminated from a model, the bond graph shows whether the coordinate representing the motion of the body becomes free to move (zero inertia) or fixed to ground (infinite inertia). This systematic interpretation of element removal makes bond graphs an attractive modelling language for automated model reduction techniques. An illustrative example is provided to demonstrate how the developed approach can be applied to provide the physical interpretation of energy element removal from a mechanical system.
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Bopche, Ghanshyam S., and Babu M. Mehtre. "Graph similarity metrics for assessing temporal changes in attack surface of dynamic networks." Computers & Security 64 (January 2017): 16–43. http://dx.doi.org/10.1016/j.cose.2016.09.010.
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Lee, Hyeonbyeong, Bokyoung Shin, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, and Jaesoo Yoo. "Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance." Applied Sciences 14, no. 11 (May 25, 2024): 4553. http://dx.doi.org/10.3390/app14114553.
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With recent advancements in network technology and the increasing popularity of the internet, the use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges, occur over time. Additionally, owing to the need for the efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and the complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.
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Chai, Wenguang, Qingfeng Luo, Zhizhe Lin, Jingwen Yan, Jinglin Zhou, and Teng Zhou. "Spatiotemporal Dynamic Multi-Hop Network for Traffic Flow Forecasting." Sustainability 16, no. 14 (July 9, 2024): 5860. http://dx.doi.org/10.3390/su16145860.
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Accurate traffic flow forecasting is vital for intelligent transportation systems, especially with urbanization worsening traffic congestion, which affects daily life, economic growth, and the environment. Precise forecasts aid in managing and optimizing transportation systems, reducing congestion, and improving air quality by cutting emissions. However, predicting outcomes is difficult due to intricate spatial relationships, nonlinear temporal patterns, and the challenges associated with long-term forecasting. Current research often uses static graph structures, overlooking dynamic and long-range dependencies. To tackle these issues, we introduce the spatiotemporal dynamic multi-hop network (ST-DMN), a Seq2Seq framework. This model incorporates spatiotemporal convolutional blocks (ST-Blocks) with residual connections in the encoder to condense historical traffic data into a fixed-dimensional vector. A dynamic graph represents time-varying inter-segment relationships, and multi-hop operation in the encoder’s spatial convolutional layer and the decoder’s diffusion multi-hop graph convolutional gated recurrent units (DMGCGRUs) capture long-range dependencies. Experiments on two real-world datasets METR-LA and PEMS-BAY show that ST-DMN surpasses existing models in three metrics.
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Wenjuan Xiao, Wenjuan Xiao, and Xiaoming Wang Wenjuan Xiao. "Attention Mechanism Based Spatial-Temporal Graph Convolution Network for Traffic Prediction." 電腦學刊 35, no. 4 (August 2024): 093–108. http://dx.doi.org/10.53106/199115992024083504007.
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<p>Considering the complexity of traffic systems and the challenges brought by various factors in traffic prediction, we propose a spatial-temporal graph convolutional neural network based on attention mechanism (AMSTGCN) to adapt to these dynamic changes and improve prediction accuracy. The model combines the spatial feature extraction capability of graph attention network (GAT) and the dynamic correlation learning capability of attention mechanism. By introducing the attention mechanism, the network can adaptively focus on the dependencies between different time steps and different nodes, effectively mining the dynamic spatial-temporal relationships in the traffic data. Specifically, we adopt an improved version of graph attention network (GAT_v2) in the spatial dimension, which allows the model to capture more complex dynamic spatial correlations. Furthermore, in the temporal dimension, we combine gated recurrent unit (GRU) structure with an attention mechanism to enhance the model’s ability to process sequential data and predict traffic flow changes over prolonged periods. To validate the effectiveness of the proposed method, extensive experiments were conducted on public traffic datasets, where AMSTGCN was compared with five different benchmark models. Experimental results demonstrate that AMSTGCN exhibits superior performance on both short-term and long-term prediction tasks and outperforms other models on multiple evaluation metrics, validating its potential and practical value in the field of traffic prediction.</p> <p> </p>
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Jiang, Yunke, and Xiaojuan Sun. "Efficient Workflow Scheduling in Edge Cloud-Enabled Space-Air-Ground-Integrated Information Systems." International Journal on Semantic Web and Information Systems 20, no. 1 (July 17, 2024): 1–29. http://dx.doi.org/10.4018/ijswis.345935.
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To address the challenges posed by the dynamism, high latency, and resource scarcity in integrated air-space-ground hybrid edge cloud environments on task completion times and node load, we designed a task scheduling system for scenarios involving the transmission and processing of interdependent tasks. This system integrates a graph neural network with attention mechanism and deep reinforcement learning. Specifically, we employ a graph encoder to extract features from DAG tasks and resources. Task scheduling solutions for dynamic environments are then generated using attention mechanism-equipped graph decoder, which are subsequently optimized based on performance metrics through the use of an Advantage Actor-Critic algorithm. Experimental results indicate that this algorithm performs well in terms of completion time and node load balance across tasks with different workflow structures, demonstrating its adaptability to highly dynamic edge cloud environments.
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Singh, Archana Tejprakash, Kaushlendra Sahu, Nishant Keshav, and Harshit Sankhwar. "Feedback Analyzer for Employee Progress Graph." International Journal of Innovative Research in Advanced Engineering 11, no. 02 (February 10, 2024): 73–77. http://dx.doi.org/10.26562/ijirae.2024.v1102.02.
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In contemporary workplace environments, the assessment and improvement of employee performance are integral components of organizational success. This abstract introduces a sophisticated Feedback Analyzer designed to streamline the process of evaluating and visualizing employee progress through dynamic and interactive graphs. The Feedback Analyzer leverages advanced data analytics and machine learning techniques to analyze feedback data collected from various sources, including performance reviews, peer evaluations, and managerial assessments. The system employs natural language processing algorithms to extract valuable insights feedback, transforming unstructured comments into quantifiable metrics. In this research we focus on the need for effective employee performance evaluation and feedback mechanisms is paramount. Traditional methods of assessing employee progress often fall short in providing real-time insights and actionable data for both employees and managers.
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Henry, Elise, Angelo Furno, and Nour-Eddin El Faouzi. "Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks." Transportation Research Record: Journal of the Transportation Research Board 2675, no. 4 (March 18, 2021): 61–78. http://dx.doi.org/10.1177/0361198121998663.
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Transport networks are essential for societies. Their proper operation has to be preserved to face any perturbation or disruption. It is therefore of paramount importance that the modeling and quantification of the resilience of such networks are addressed to ensure an acceptable level of service even in the presence of disruptions. The paper aims at characterizing network resilience through weighted degree centrality. To do so, a real dataset issued from probe vehicle data is used to weight the graph by the traffic load. In particular, a set of disrupted situations retrieved from the study dataset is analyzed to quantify the impact on network operations. Results demonstrate the ability of the proposed metrics to capture traffic dynamics as well as their utility for quantifying the resilience of the network. The proposed methodology combines different metrics from the complex networks theory (i.e., heterogeneity, density, and symmetry) computed on temporal and weighted graphs. Time-varying traffic conditions and disruptions are analyzed by providing relevant insights on the network states via three-dimensional maps.
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Zhang, Yihe, Sheng Zhang, Jaime S. Ide, Sien Hu, Simon Zhornitsky, Wuyi Wang, Guozhao Dong, Xiaoying Tang, and Chiang-shan R. Li. "Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time." NeuroImage: Clinical 18 (2018): 793–801. http://dx.doi.org/10.1016/j.nicl.2018.03.016.
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Chen, Chin-Yi, and Jih-Jeng Huang. "Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems." Future Internet 15, no. 10 (September 28, 2023): 323. http://dx.doi.org/10.3390/fi15100323.
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Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking algorithm that addresses the limitations of existing models. TKGRS uniquely integrates graph convolutional networks (GCNs), matrix factorization, and temporal decay factors to offer a robust and dynamic recommendation mechanism. The algorithm’s architecture comprises an initial embedding layer for identifying the user and item, followed by a GCN layer for a nuanced understanding of the relationships and fully connected layers for prediction. A temporal decay factor is also used to give weightage to recent user–item interactions. Empirical validation using the MovieLens 100K, 1M, and Douban datasets showed that TKGRS outperformed the state-of-the-art models according to the evaluation metrics, i.e., RMSE and MAE. This innovative approach sets a new standard in movie recommendation systems and opens avenues for future research in advanced graph algorithms and machine learning techniques.
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Cao, Yang, Detian Liu, Qizheng Yin, Fei Xue, and Hengliang Tang. "MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting." Journal of Advanced Transportation 2022 (June 17, 2022): 1–15. http://dx.doi.org/10.1155/2022/2811961.
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Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.
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Elmezain, Mahmoud, Ebtesam A. Othman, and Hani M. Ibrahim. "Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis." Mathematics 9, no. 22 (November 10, 2021): 2850. http://dx.doi.org/10.3390/math9222850.
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In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between nodes are an important concept in a social network. The new centrality measures are proposed for weighted networks, which relies on a time-ordered weighted graph model, generalized temporal degree and closeness centrality. Furthermore, two measures—Temporal Degree-Degree and Temporal Closeness-Closeness—are employed to better understand the significance of nodes in weighted dynamic networks. Our study is caried out according to real dynamic weighted networks dataset of a university-based karate club. Through extensive experiments and discussions of the proposed metrics, our analysis proves that there is an effectiveness on the impact of each node throughout social networks.
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Luo, L., Y. Yang, Q. Gong, and F. Li. "Different alternations of static and dynamic brain regional topological metrics in schizophrenia and obsessive-compulsive disorder." European Psychiatry 64, S1 (April 2021): S522—S523. http://dx.doi.org/10.1192/j.eurpsy.2021.1397.
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IntroductionThough schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are conceptualized as distinct clinical entities, they do have notable symptom overlap and a tight association. Graph-theoretical analysis of the brain connectome provides more indicators to describe the functional organization of the brain, which may help us understand the shared and disorder-specific neural basis of the two disorders.ObjectivesTo explore the static and dynamic topological organization of OCD and SZ as well as the relationship between topological metrics and clinical variables.MethodsResting state functional magnetic resonance imaging data of 31 OCD patients, 49 SZ patients, and 45 healthy controls (HC) were involved in this study (Table 1). Using independent component analysis to obtain independent components (ICs) (Figure 1), which were defined as nodes for static and dynamic topological analysis.ResultsStatic analysis showed the global efficiency of SZ was higher than HC. For nodal degree centrality, OCD exhibited decreased degree centrality in IC59 (located in visiual network) (P = 0.03) and increased degree centrality in IC38 (located in salience network) (P = 0.002) compared with HC. Dynamic analysis showed OCD exhibited decreased dynamics of degree centrality in IC38 (P = 0.003) compared with HC, which showed a negative correlation with clinical scores in OCD. While SZ showed decreased dynamics of degree centrality in IC76 (located in sensory motor network) compared with OCD (P=0.009), which showed a positive correlation with clinical scores in SZ (Figure 2).ConclusionsThese changes are suggestive of disorder-specific alternation of static and dynamic brain topological organization in OCD and SZ.
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Shivangni Jat. "Graph Theoretical Models for Enhancing Highway Connectivity and Safety in Vehicular Networks." Communications on Applied Nonlinear Analysis 31, no. 4s (July 5, 2024): 196–218. http://dx.doi.org/10.52783/cana.v31.839.
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Vehicular networks play a crucial role in modern transportation systems, significantly impacting connectivity and safety on highways. This paper explores the application of graph theoretical models to enhance both connectivity and safety in vehicular networks. Graph theory, a branch of discrete mathematics, provides a robust framework for modeling and analyzing complex networks, including those formed by vehicles on highways. Our study begins by defining the vehicular network as a graph where nodes represent vehicles, and edges denote communication links between them. We employ various graph theoretical concepts such as connectivity, centrality, and network flow to evaluate and improve the network's performance. Key metrics, including the degree of nodes, clustering coefficients, and shortest path lengths, are utilized to quantify network connectivity and identify critical nodes and edges that influence overall network efficiency. One of the primary objectives is to ensure uninterrupted connectivity in the presence of dynamic and often unpredictable vehicular movement. To this end, we analyze the network's resilience to node failures and propose strategies to enhance robustness using redundancy and alternative routing paths. By incorporating concepts like k-connectivity and network diameter, we develop models that maintain high levels of connectivity despite the removal or failure of multiple nodes or edges. Safety is addressed through the lens of network stability and reliability. We investigate the impact of vehicular density, speed, and communication range on the network's ability to sustain reliable communication channels. Techniques such as dynamic topology management and adaptive power control are proposed to mitigate the risks associated with network fragmentation and communication delays. Furthermore, we introduce optimization algorithms that leverage graph partitioning and community detection to improve the management of vehicular clusters, facilitating efficient data dissemination and reducing the likelihood of congestion-related incidents. The proposed models are validated through simulations that mimic real-world highway conditions, demonstrating significant improvements in both connectivity and safety metrics. In conclusion, the application of graph theoretical models offers a promising approach to enhancing highway connectivity and safety in vehicular networks
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Piao, Yinghao, and Jin-Xi Zhang. "Text Triplet Extraction Algorithm with Fused Graph Neural Networks and Improved Biaffine Attention Mechanism." Applied Sciences 14, no. 8 (April 22, 2024): 3524. http://dx.doi.org/10.3390/app14083524.
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In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks with an enhanced biaffine attention mechanism. This model amalgamates the sophisticated capabilities of both graph attention and convolutional networks to process graph-structured data, substantially enhancing the interpretation and extraction of textual features. By optimizing the biaffine attention mechanism, the model adeptly uncovers the subtle interplay between aspect terms and emotional expressions, offering enhanced flexibility and superior contextual analysis through dynamic weight distribution. A series of comparative experiments confirm the model’s significant performance improvements across various metrics, underscoring its efficacy and refined effectiveness in ABSA tasks.
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Sighencea, Bogdan Ilie, Ion Rareș Stanciu, and Cătălin Daniel Căleanu. "D-STGCN: Dynamic Pedestrian Trajectory Prediction Using Spatio-Temporal Graph Convolutional Networks." Electronics 12, no. 3 (January 26, 2023): 611. http://dx.doi.org/10.3390/electronics12030611.
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Predicting pedestrian trajectories in urban scenarios is a challenging task that has a wide range of applications, from video surveillance to autonomous driving. The task is difficult since pedestrian behavior is affected by both their individual path’s history, their interactions with others, and with the environment. For predicting pedestrian trajectories, an attention-based interaction-aware spatio-temporal graph neural network is introduced. This paper introduces an approach based on two components: a spatial graph neural network (SGNN) for interaction-modeling and a temporal graph neural network (TGNN) for motion feature extraction. The SGNN uses an attention method to periodically collect spatial interactions between all pedestrians. The TGNN employs an attention method as well, this time to collect each pedestrian’s temporal motion pattern. Finally, in the graph’s temporal dimension characteristics, a time-extrapolator convolutional neural network (CNN) is employed to predict the trajectories. Using a lower variable size (data and model) and a better accuracy, the proposed method is compact, efficient, and better than the one represented by the social-STGCNN. Moreover, using three video surveillance datasets (ETH, UCY, and SDD), D-STGCN achieves better experimental results considering the average displacement error (ADE) and final displacement error (FDE) metrics, in addition to predicting more social trajectories.
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Wang, Bingxing, Hongye Zheng, Yingbin Liang, Guanming Huang, and Junliang Du. "Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification." International Journal of Innovative Research in Computer Science and Technology 12, no. 5 (September 2024): 94–99. http://dx.doi.org/10.55524/ijircst.2024.12.5.13.
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For the intricate task of multi-label image classification, this paper introduces an innovative approach: an attention-guided dual-branch dynamic graph convolutional network. This methodology is designed to address the difficulties faced by current models when handling multiple labels within images. By integrating multi-scale features, it enhances the retention of original category information and boosts the robustness of feature learning. Utilizing a semantic attention module, the study dynamically reweights feature categories in the training dataset, enhancing the network's capability to identify smaller objects and generate context-sensitive category representations. The effectiveness of the proposed model was evaluated using the MS-COCO2014 imagery dataset, demonstrating superior performance in critical metrics such as classification precision (CP), recall (CR), and F1 score (CF1), outperforming other state-of-the-art models. Furthermore, a cascaded classification structure was implemented to leverage the prior information from static images to inform the processing of dynamic ones, and to utilize original image category data to augment label correlations, thereby enhancing overall classification accuracy.
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Han, Shi-Yuan, Qiang Zhao, Qi-Wei Sun, Jin Zhou, and Yue-Hui Chen. "EnGS-DGR: Traffic Flow Forecasting with Indefinite Forecasting Interval by Ensemble GCN, Seq2Seq, and Dynamic Graph Reconfiguration." Applied Sciences 12, no. 6 (March 11, 2022): 2890. http://dx.doi.org/10.3390/app12062890.
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An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network (GCN), sequence-to-sequence (Seq2Seq) learning model, and dynamic graph reconfiguration (DGR) algorithm. At the first stage, instead of employing entire nodes in the traffic network, the DGR algorithm is proposed to reconstruct the traffic graph topology consisting of traffic nodes with tight correlation under a specific forecasting interval, where the degree of correlation among the traffic nodes is quantized from the perspective of multi-view clustering. At the second stage, GCN-Seq2Seq integration strategy is introduced to extract the data of the spatio-temporal dependence and forecast traffic flow. We applied the proposed EnGS-DGR to two different datasets from the highways of Los Angeles County and of California’s Bay Area; the simulation results show that the proposed EnGS-DGR is superior to other eight popular models for traffic flow forecasting in terms of three common performance metrics.
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Li, Shibo, Jiajun Xu, Xinqiang Chen, Yajie Zhang, Yiwen Zheng, and Octavian Postolache. "Maritime Traffic Knowledge Discovery via Knowledge Graph Theory." Journal of Marine Science and Engineering 12, no. 12 (December 19, 2024): 2333. https://doi.org/10.3390/jmse12122333.
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Intelligent ships are a key focus for the future development of maritime transportation, relying on efficient decision-making and autonomous control within complex environments. To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. The study specifically targets the extraction and modeling of critical events, including variations in ship speed, course changes, vessel encounters, and port entries and exits. To evaluate the urgency of encounters, mathematical algorithms are applied to the Distance to Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA) metrics. Furthermore, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to identify suitable docking berths. Additionally, multi-source meteorological data are integrated with ship dynamic data, providing a more comprehensive representation of the maritime environment. The resulting knowledge system effectively combines ship attributes, navigational status, event relationships, and environmental factors, thereby offering a robust framework for supporting intelligent ship operations.
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Wang, Zhiqiong, Zican Lin, Shuo Li, Yibo Wang, Weiying Zhong, Xinlei Wang, and Junchang Xin. "Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer’s Disease." Applied Sciences 13, no. 14 (July 21, 2023): 8433. http://dx.doi.org/10.3390/app13148433.
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Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To improve the accuracy of Alzheimer’s disease diagnosis, we propose a new network architecture called Dynamic Multi-Task Graph Isomorphism Network (DMT-GIN). This approach uses fMRI images transformed into brain network structures to classify Alzheimer’s disease more effectively. In the DMT-GIN architecture, we integrate an attention mechanism with the Graph Isomorphism Network (GIN) to capture node features and topological structure information. To further enhance AD classification performance, we incorporate auxiliary tasks of gender and age classification prediction alongside the primary AD classification task in the network. This is achieved through sharing network parameters and adaptive weight adjustments for simultaneous task optimization. Additionally, we introduce a method called GradNorm for dynamically balancing gradient updates between tasks. Evaluation results demonstrate that the DMT-GIN model outperforms existing baseline methods on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, leading in various metrics with a prediction accuracy of 90.44%. This indicates that our DMT-GIN model effectively captures brain network features, providing a powerful auxiliary means for the early diagnosis of Alzheimer’s disease.
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Panilov, Pavel Alekseevich. "Cognitive Reframing in Anticipation and Prevention of Multiplex Threats to Critical Infrastructure." Virtual Communication and Social Networks 3, no. 4 (December 17, 2024): 316–25. https://doi.org/10.21603/2782-4799-2024-3-4-316-325.
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The article introduces a new cognitive reframing approach to anticipating and preventing multiplex threats to critical infrastructure. In the context of constantly evolving threats, the model may increase the effectiveness of incident prevention strategies. It is visualized as a graph with nodes for concepts of cognitive reframing and edges for the connections between them. The model includes weight values that depend on the importance of each concept, as well as additional importance metrics, coefficients, and interactions. By calculating the edge weights, the authors developed a graph that illustrates the interrelationships between the concepts. The model can be applied to various scenarios as it improves cybersecurity, responds to natural disasters, and ensures the smooth operation of various systems. The model takes into account dynamic factors, multiple importance metrics, interactions, and statistical methods, which makes it flexible and adaptive. Extra factors could increase the complexity, accuracy, and adaptability of the current model. Cognitive reframing has good prospects in the field of critical infrastructure while the new model proves to be an effective threat management tool.
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Qiu, Dehong, Qifeng Zhang, and Shaohong Fang. "Reconstructing Software High-Level Architecture by Clustering Weighted Directed Class Graph." International Journal of Software Engineering and Knowledge Engineering 25, no. 04 (May 2015): 701–26. http://dx.doi.org/10.1142/s0218194015500072.
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Software architecture reconstruction plays an important role in software reuse, evolution and maintenance. Clustering is a promising technique for software architecture reconstruction. However, the representation of software, which serves as clustering input, and the clustering algorithm need to be improved in real applications. The representation should contain appropriate and adequate information of software. Furthermore, the clustering algorithm should be adapted to the particular demands of software architecture reconstruction well. In this paper, we first extract Weighted Directed Class Graph (WDCG) to represent object-oriented software. WDCG is a structural and quantitative representation of software, which contains not only the static information of software source code but also the dynamic information of software execution. Then we propose a WDCG-based Clustering Algorithm (WDCG-CA) to reconstruct high-level software architecture. WDCG-CA makes full use of the structural and quantitative information of WDCG, and avoids wrong compositions and arbitrary partitions successfully in the process of reconstructing software architecture. We introduce four metrics to evaluate the performance of WDCG-CA. The results of the comparative experiments show that WDCG-CA outperforms the comparative approaches in most cases in terms of the four metrics.
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Joshi, Manas, Arshdeep Singh, Sayan Ranu, Amitabha Bagchi, Priyank Karia, and Puneet Kala. "FoodMatch: Batching and Matching for Food Delivery in Dynamic Road Networks." ACM Transactions on Spatial Algorithms and Systems 8, no. 1 (March 31, 2022): 1–25. http://dx.doi.org/10.1145/3494530.
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Food delivery, today, is a multi-billion dollar industry. Minimizing food delivery time is a key contributor towards building positive customer experiences. More precisely, given a stream of food orders and available delivery vehicles, how should orders be assigned to vehicles so the delivery time is minimized? Several decisions have to be made: (1) assignment of orders to vehicles, (2) grouping orders into batches to cope with limited vehicle availability, (3) adapting to dynamic positions of delivery vehicles, and (4) ensuring scalability to the demands of real-world workloads. We show that the minimization problem is not only NP-hard but inapproximable in polynomial time. To mitigate this computational bottleneck, we develop an algorithm called FoodMatch , which maps the vehicle assignment problem to that of minimum weight perfect matching on a bipartite graph. To further reduce the quadratic construction cost of the bipartite graph, we deploy best-first search to only compute a subgraph that is highly likely to contain the minimum matching. The solution quality is further enhanced by reducing batching to a graph batching problem and anticipating dynamic positions of vehicles through angular distance . We perform extensive experiments on real food-delivery data from large metropolitan cities. Our results establish that FoodMatch imparts substantial improvements over baseline strategies across a host of metrics such as food delivery time, waiting time at restaurants, and orders delivered per kilometer. Furthermore, FoodMatch is efficient enough to handle real-world workloads.
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Wu, Wenhong, and Yunkai Kang. "Ensemble Empirical Mode Decomposition Granger Causality Test Dynamic Graph Attention Transformer Network: Integrating Transformer and Graph Neural Network Models for Multi-Sensor Cross-Temporal Granularity Water Demand Forecasting." Applied Sciences 14, no. 8 (April 18, 2024): 3428. http://dx.doi.org/10.3390/app14083428.
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Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and Graph Neural Networks. It employs the EEMD–Granger test to delineate the interconnections among sensors and extracts the spatiotemporal features within the causal domain by stacking dynamical graph spatiotemporal attention layers. The experimental results demonstrate that compared to baseline models, the EG-DGATN improves the MAPE metrics by 2.12%, 4.33%, and 6.32% in forecasting intervals of 15 min, 45 min, and 90 min, respectively. The model achieves an R2 score of 0.97, indicating outstanding predictive accuracy and exceptional explanatory power for the target variable. This research highlights significant potential applications in predictive tasks within smart water management systems.
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Peng, Wei, Xiaopeng Hong, Haoyu Chen, and Guoying Zhao. "Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2669–76. http://dx.doi.org/10.1609/aaai.v34i03.5652.
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Human action recognition from skeleton data, fuelled by the Graph Convolutional Network (GCN) with its powerful capability of modeling non-Euclidean data, has attracted lots of attention. However, many existing GCNs provide a pre-defined graph structure and share it through the entire network, which can loss implicit joint correlations especially for the higher-level features. Besides, the mainstream spectral GCN is approximated by one-order hop such that higher-order connections are not well involved. All of these require huge efforts to design a better GCN architecture. To address these problems, we turn to Neural Architecture Search (NAS) and propose the first automatically designed GCN for this task. Specifically, we explore the spatial-temporal correlations between nodes and build a search space with multiple dynamic graph modules. Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. Moreover, a corresponding sampling- and memory-efficient evolution strategy is proposed to search in this space. The resulted architecture proves the effectiveness of the higher-order approximation and the layer-wise dynamic graph modules. To evaluate the performance of the searched model, we conduct extensive experiments on two very large scale skeleton-based action recognition datasets. The results show that our model gets the state-of-the-art results in term of given metrics.
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Qu, Shuhui, Kasra Yazdani, and Janghwan Lee. "32‐3: Heterogeneous Resource Constrained Reinforcement Learning Photolithography Scheduler with Heterogeneous Graph Attention Network." SID Symposium Digest of Technical Papers 55, no. 1 (June 2024): 417–20. http://dx.doi.org/10.1002/sdtp.17546.
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In mass manufacturing of OLED displays, photolithography process is a critical bottleneck in production capacity. As a result, effective scheduling of the photolithography process is crucial to the overall throughput, productivity, and efficiency of the production. The processing of OLED panels depends upon heterogeneous resources. Aside from that, the complexity, stochastic nature, and highly dynamic characteristic of the processpresent considerable challenges. To address these, we propose a new framework employing a heterogeneous graph neural networkbased Rainbow algorithm. This framework optimizes the schedule used in photolithography process. Considering the constraints on machines, masks, and the stochastic nature of arrival processes, we optimize for maximizing productivity while minimizing the associated costs. We model the interactions among product lot steps, machines, and masks as a heterogeneous graph. This graph encodes the information of lot steps, machines, and masks into distinctive nodes. We then implement a Graph Attention Networkbased architecture for deep representation learning. This transforms state information into node embeddings for each step of each product lot, facilitating decision‐making. Reinforcement learning agents leverage these embeddings to prioritize products via a parameterized Q‐function. Our extensive experiments demonstrate the superior performance of our approach across multiple evaluation metrics in both static and dynamic environment benchmarks, with a higher winning rate and scheduling reward return when compared to existing reinforcement learning methods.
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Geng, Shijie, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li, and Anoop Cherian. "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1415–23. http://dx.doi.org/10.1609/aaai.v35i2.16231.
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Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancements into which could influence several human-machine interaction applications. To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and producing representations conditioned on the input question. Our proposed Transformer variant uses a shuffling scheme on their multi-head outputs, demonstrating better regularization. To encode fine-grained visual information, we present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing spatio-semantic graph representations for every frame, and an inter-frame aggregation module capturing temporal cues. Our entire pipeline is trained end-to-end. We present experiments on the benchmark AVSD dataset, both on answer generation and selection tasks. Our results demonstrate state-of-the-art performances on all evaluation metrics.
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Xu, Yi, Junjie Ou, Hui Xu, and Luoyi Fu. "Temporal Knowledge Graph Reasoning with Historical Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4765–73. http://dx.doi.org/10.1609/aaai.v37i4.25601.
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Temporal knowledge graph, serving as an effective way to store and model dynamic relations, shows promising prospects in event forecasting. However, most temporal knowledge graph reasoning methods are highly dependent on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current moment is often the combined effect of a small part of historical information and those unobserved underlying factors. To this end, we propose a new event forecasting model called Contrastive Event Network (CENET), based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities that can best match the given query. Simultaneously, it trains representations of queries to investigate whether the current moment depends more on historical or non-historical events by launching contrastive learning. The representations further help train a binary classifier whose output is a boolean mask to indicate related entities in the search space. During the inference process, CENET employs a mask-based strategy to generate the final results. We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets.
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Abbas, Khushnood, Alireza Abbasi, Shi Dong, Ling Niu, Liyong Chen, and Bolun Chen. "A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks." Entropy 25, no. 2 (January 31, 2023): 257. http://dx.doi.org/10.3390/e25020257.
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Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given’s angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein–protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics.
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Liang, Qianhui, Anandhi Bharadwaj, and Bu Sung Lee. "Interactive and Iterative Service-Composition-Based Approach to Flexible Information System Development." International Journal of Web Services Research 8, no. 4 (October 2011): 81–107. http://dx.doi.org/10.4018/jwsr.2011100104.
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An emerging class of technologies defined as Service-Oriented Architecture (SOA) has been heralded as the answer for inflexible IT architecture and promises to reduce operational barriers of current IT infrastructures. In SOA, loosely coupled Web services are integrated to provide dynamic digital capabilities within and across enterprise boundaries. Little research exists on development processes of information systems using Web services and against certain development metrics. One way to perform such research is to propose a development approach, identify the metrics, and embed the metrics into the technique of service composition to allow system development with desired characteristics. This paper reports an approach to information system development based on Web services composition and the metrics designed for such approaches. This approach is based on semi-automatic, interactive, and iterative Web service composition -- a hybrid technique based on developing and searching an AND/OR graph for composite services discovery while taking into consideration human judgment for solution selection and validation by interactions in an iterative way. The composition process leverages historical Web service usage data and provides helpful suggestions to the users regarding available component services. The authors propose that the metrics can investigate the characteristics of such development approaches.
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Geetanjali Kale (Rao). "BUGTGNN: Bayesian Uncertainty and Game Theory for Enhancing Graph Neural Networks through Mathematical Analysis." Advances in Nonlinear Variational Inequalities 28, no. 2 (October 9, 2024): 84–105. http://dx.doi.org/10.52783/anvi.v28.1853.
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The ongoing COVID-19 pandemic has emphasized the critical need for rapid and accurate diagnostic methods. Precise classification of chest X-ray images into COVID-19 and non-COVID-19 cases serves as a pivotal tool in effective disease management and control. Existing methods often suffer from trade-offs between accuracy, precision, and computational efficiency, hindering their practical utility. Current approaches mainly rely on traditional machine learning algorithms or Convolutional Neural Networks (CNNs), which while effective, still present limitations in terms of sensitivity, specificity, and computational speed. These constraints necessitate the exploration of innovative techniques for improving classification metrics across multiple dimensions. In this work, we introduce a novel framework for optimizing Graph Neural Networks (GNNs) through mathematical analysis, specifically incorporating spectral methods, dynamic graph sparsification, game-theoretic attention mechanisms, Bayesian uncertainty models, and advanced graph partitioning techniques. When applied to the classification of COVID-19 chest X-rays, our model demonstrated significant improvements—increasing precision by 8.3%, accuracy by 8.5%, recall by 4.9%, specificity by 4.5%, and the Area Under the Curve (AUC) by 5.9%, while simultaneously reducing computational delay by 10.5% across multiple datasets. The proposed optimization strategies showcase the power of interdisciplinary approaches in advancing machine learning techniques for medical applications. The demonstrated improvements in classification metrics and computational efficiency highlight the model's potential for broader adoption in healthcare settings, providing a robust, fast, and more accurate tool for COVID-19 diagnosis.
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Li, Yi, and Min Huang. "Identification of Critical Road Links Based on Static and Dynamic Features Fusion." Applied Sciences 13, no. 10 (May 13, 2023): 5994. http://dx.doi.org/10.3390/app13105994.
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Traffic congestion is a significant challenge in modern cities, leading to economic losses, environmental pollution, and inconvenience for the public. Identifying critical road links in a city can assist urban traffic management in developing effective management strategies, preserving the efficiency of critical road links, and ensuring the smooth operation of urban transportation systems. However, the existing road link importance evaluation metrics mostly rely on complex network metrics and traffic metrics, which may lead to biased results. In this paper, we propose a critical road link identification framework based on the fusion of dynamic and static features. First, we propose a directed dual topological traffic network model that considers the subjectivity of road links, traffic circulation characteristics, and time-varying characteristics, which addresses the limitations of existing traffic network topology construction. Subsequently, we employ a novel graph representation learning network to learn the road link node low-dimensional embeddings. Finally, we utilize clustering algorithms to cluster each road link node and evaluate critical road links using the average importance evaluation indicator of different categories. The results of comparison experiments using real-world data demonstrate the clear superiority and effectiveness of our proposed method. Specifically, our method is able to achieve a reduction in traffic network efficiency of 70–75% when less than 25% of the road links are removed. In contrast, the other baseline methods only achieve a reduction of 50–70% when removing the same proportion of road links. These findings highlight the significant advantages of our approach in identifying the critical links.
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Lu, Jiaming, Chuanyang Hong, and Rui Wang. "MAGT-toll: A multi-agent reinforcement learning approach to dynamic traffic congestion pricing." PLOS ONE 19, no. 11 (November 18, 2024): e0313828. http://dx.doi.org/10.1371/journal.pone.0313828.
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Modern urban centers have one of the most critical challenges of congestion. Traditional electronic toll collection systems attempt to mitigate this issue through pre-defined static congestion pricing methods; however, they are inadequate in addressing the dynamic fluctuations in traffic demand. Dynamic congestion pricing has been identified as a promising approach, yet its implementation is hindered by the computational complexity involved in optimizing long-term objectives and the necessity for coordination across the traffic network. To address these challenges, we propose a novel dynamic traffic congestion pricing model utilizing multi-agent reinforcement learning with a transformer architecture. This architecture capitalizes on its encoder-decoder structure to transform the multi-agent reinforcement learning problem into a sequence modeling task. Drawing on insights from research on graph transformers, our model incorporates agent structures and positional encoding to enhance adaptability to traffic flow dynamics and network coordination. We have developed a microsimulation-based environment to implement a discrete toll-rate congestion pricing scheme on actual urban roads. Our extensive experimental results across diverse traffic demand scenarios demonstrate substantial improvements in congestion metrics and reductions in travel time, thereby effectively alleviating traffic congestion.
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Nazarevych, Valerii, Artem Mykytiuk, Olha Shevchuk, and Ihor Kulyk. "A method of secure network traffic routing based on specified criterias." Collection "Information Technology and Security" 11, no. 2 (December 28, 2023): 156–65. http://dx.doi.org/10.20535/2411-1031.2023.11.2.293752.
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Due to the implementation of new network services, the increase amount of data that need to be transmitted, and the use of networks in various sectors with diverse communication requirements, there is a need to develop new approaches to ensure the quality of such communications. Leading network equipment manufacturers and standardization organizations are developing new routing algorithms, resulting in the introduction of new routing protocols or improvements to existing ones. However, all these algorithms cover routing principles for general-purpose networks and do not consider the communication requirements of specialized networks. Therefore, the task arises to research optimization directions for network traffic routing, define optimization criteria, and further develop a method for secure network traffic routing based on the specified criteria. In this work, a routing method is proposed that takes into account the defined requirements when searching for the optimal route. In the case of dynamic routing, each router calculates the shortest routes to all other networks based on the shortest path search algorithm. This work defines a method for calculating metrics based on specified criteria and formally describes the algorithm for finding the shortest path. Quality of communication criteria is introduced, which will enable meeting communication requirements in specialized networks. Calculation methods for these criteria are demonstrated, and data collection methods for the calculation of specified criteria are determined. A formula for calculating metrics is proposed, which includes the possibility of selecting T-values and determining their numerical parameters to prioritize specific criteria. Default values for criteria are defined, and metric calculations are tested by default for different types of interfaces. After calculating metrics, the task reduces to finding the shortest paths in a weighted graph using an algorithm based on Dijkstra's algorithm. The proposed algorithm for finding the shortest path involves identifying the primary (shortest) and backup paths from a given source vertex to all other graph vertices. A formal description of the proposed algorithm is provided.
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Koubiyr, Ismail, Mathilde Deloire, Pierre Besson, Pierrick Coupé, Cécile Dulau, Jean Pelletier, Thomas Tourdias, et al. "Longitudinal study of functional brain network reorganization in clinically isolated syndrome." Multiple Sclerosis Journal 26, no. 2 (November 27, 2018): 188–200. http://dx.doi.org/10.1177/1352458518813108.
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Background: There is a lack of longitudinal studies exploring the topological organization of functional brain networks at the early stages of multiple sclerosis (MS). Objective: This study aims to assess potential brain functional reorganization at rest in patients with CIS (PwCIS) after 1 year of evolution and to characterize the dynamics of functional brain networks at the early stage of the disease. Methods: We prospectively included 41 PwCIS and 19 matched healthy controls (HCs). They were scanned at baseline and after 1 year. Using graph theory, topological metrics were calculated for each region. Hub disruption index was computed for each metric. Results: Hub disruption indexes of degree and betweenness centrality were negative at baseline in patients ( p < 0.05), suggesting brain reorganization. After 1 year, hub disruption indexes for degree and betweenness centrality were still negative ( p < 0.00001), but such reorganization appeared more pronounced than at baseline. Different brain regions were driving these alterations. No global efficiency differences were observed between PwCIS and HCs either at baseline or at 1 year. Conclusion: Dynamic changes in functional brain networks appear at the early stages of MS and are associated with the maintenance of normal global efficiency in the brain, suggesting a compensatory effect.
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Long, Hao, Feng Hu, and Lingjun Kong. "Enhanced Link Prediction and Traffic Load Balancing in Unmanned Aerial Vehicle-Based Cloud-Edge-Local Networks." Drones 8, no. 10 (September 27, 2024): 528. http://dx.doi.org/10.3390/drones8100528.
Full textAbstract:
With the advancement of cloud-edge-local computing, Unmanned Aerial Vehicles (UAVs), as flexible mobile nodes, offer novel solutions for dynamic network deployment. However, existing research on UAV networks faces substantial challenges in accurately predicting link dynamics and efficiently managing traffic loads, particularly in highly distributed and rapidly changing environments. These limitations result in inefficient resource allocation and suboptimal network performance. To address these challenges, this paper proposes a UAV-based cloud-edge-local network resource elastic scheduling architecture, which integrates the Graph-Autoencoder–GAN-LSTM (GA–GLU) algorithm for accurate link prediction and the FlowBender-Enhanced Reinforcement Learning for Load Balancing (FERL-LB) algorithm for dynamic traffic load balancing. GA–GLU accurately predicts dynamic changes in UAV network topologies, enabling adaptive and efficient scheduling of network resources. FERL-LB leverages these predictions to optimize traffic load balancing within the architecture, enhancing both performance and resource utilization. To validate the effectiveness of GA–GLU, comparisons are made with classical methods such as CN and Katz, as well as modern approaches like Node2vec and GAE–LSTM, which are commonly used for link prediction. Experimental results demonstrate that GA–GLU consistently outperforms these competitors in metrics such as AUC, MAP, and error rate. The integration of GA–GLU and FERL-LB within the proposed architecture significantly improves network performance in highly dynamic environments.
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Li, Wei, Xi Zhan, Xin Liu, Lei Zhang, Yu Pan, and Zhisong Pan. "SASTGCN: A Self-Adaptive Spatio-Temporal Graph Convolutional Network for Traffic Prediction." ISPRS International Journal of Geo-Information 12, no. 8 (August 18, 2023): 346. http://dx.doi.org/10.3390/ijgi12080346.
Full textAbstract:
Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks and sequence prediction methods to extract complicated spatio-temporal patterns statically. However, they neglect to account for dynamic underlying correlations and thus fail to produce satisfactory prediction results. Therefore, we propose a novel Self-Adaptive Spatio-Temporal Graph Convolutional Network (SASTGCN) for traffic prediction. A self-adaptive calibrator, a spatio-temporal feature extractor, and a predictor comprise the bulk of the framework. To extract the distribution bias of the input in the self-adaptive calibrator, we employ a self-supervisor made of an encoder–decoder structure. The concatenation of the bias and the original characteristics are provided as input to the spatio-temporal feature extractor, which leverages a transformer and graph convolution structures to learn the spatio-temporal pattern, and then applies a predictor to produce the final prediction. Extensive trials on two public traffic prediction datasets (METR-LA and PEMS-BAY) demonstrate that SASTGCN surpasses the most recent techniques in several metrics.
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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.
Full textAbstract:
In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.