Academic literature on the topic 'Dynamic Graph Metrics'
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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 (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 (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 (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 (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, et al. "A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning." Education Sciences 13, no. 2 (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 (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 (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 (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, et al. "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 (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.
Dissertations / Theses on the topic "Dynamic Graph Metrics"
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Bridonneau, Vincent. "Generation and Analysis of Dynamic Graphs." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH23.
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La nature et les sociétés humaines offrent de nombreux exemples de systèmes composés d'entités qui interagissent, communiquent ou sont simplement connectées les unes aux autres. La théorie des graphes offre un excellent formalisme pour modéliser ces systèmes complexes, allant des réseaux sociaux aux systèmes biologiques. La plupart des phénomènes observés dans ces réseaux peuvent s'exprimer sous forme de propriétés sur les graphes. On peut notamment citer le phénomène du « petit monde » ou les réseaux dits « sans échelle ». Comprendre les mécanismes sous-jacents à leur évolution est essentiel pour saisir les dynamiques de ces réseaux. Différents mécanismes existent pour reproduire les propriétés observées. Parmi eux, on peut citer l'attachement préférentiel, utilisé notamment par le modèle de Barabasi-Albert (BA), qui permet de produire des séquences de graphes croissants sans échelle. Dans une direction parallèle, on peut également étendre le concept de graphe en y ajoutant une dimension temporelle. Dans ce cas, les propriétés statiques des graphes sont retravaillées pour tenir compte de l'évolution des graphes dans le temps. Par exemple, on peut citer la notion de trajet qui, semblable à celle de chemin, traduit la possibilité de se déplacer d'un sommet à un autre en respectant des contraintes temporelles. De même que dans le cas des réseaux complexes, la capacité à générer des graphes temporels est étudiée afin de produire des graphes aux propriétés spécifiques.On peut par exemple évoquer le modèle Edge-Markovian Graph, un processus stochastique permettant de produire des graphes et d’étudier des problèmes de communication. L'observation de ces mécanismes de génération donne naissance à la problématique de cette thèse, qui réside dans l'étude de processus itératifs de génération de graphes temporels. Lorsqu'un graphe est obtenu par itérations successives d'un tel mécanisme, on parle d'un graphe dynamique. Cette dénomination met en avant l'aspect itératif du processus pour produire une séquence ordonnée de graphes. Une question nous a particulièrement intéressés dans le cadre de ce travail : que se passe-t-il lorsqu’un générateur n'est soumis à aucune contrainte, notamment en ce qui concerne l'évolution du nombre de sommets au fil du temps ? Cette situation soulève deux problématiques : la possibilité qu'un processus conduise à des graphes périodiques au-delà d'un certain moment et la quantification des changements entre deux étapes consécutives du processus. Pour répondre à ces interrogations, nous avons introduit deux métriques.La première, que nous avons appelé sustainability, et que l'on peut traduire par pérennité, est une mesure qualitative : un générateur est dit sustainable s'il produit des graphes qui ne deviennent ni vides ni périodiques. La seconde métrique, le DynamicScore, quantifie les changements entre deux instants successifs, à la fois au niveau des sommets (V-DynamicScore) et des arêtes (E-DynamicScore). Pour démontrer la pertinence de la notion de pérennité, nous avons défini et étudié un générateur de graphes mettant en évidence les nombreux défis rencontrés lors de l'exploration de cette notion. En ce qui concerne le DynamicScore, nous l'avons testé sur divers générateurs ainsi que sur des données réelles, démontrant sa capacité à capturer la dynamique d’un réseau, qu’il soit artificiel ou réel. L’étude de ces deux concepts a ouvert la voie à de nombreuses nouvelles questions et renforcé les liens entre l’analyse des réseaux complexes et la théorie des graphes temporels<br>In this thesis, we investigate iterative processes producing a flow of graphs. These processes findapplications both in complex networks and time-varying graphs. Starting from an initial configurationcalled a seed, these processes produce a continuous flow of graphs. A key question arises when theseprocesses impose no constraints on the size of the generated graphs: under what conditions can we ensurethat the graphs do not become empty? And how can we account for the changes between successive stepsof the process? To address the first question, we introduced the concept of sustainability, which verifieswhether an iterative process is likely to produce graphs with periodic behaviors. We defined and studied agraph generator that highlights the many challenges encountered when exploring this notion. Regardingthe second question, we designed a metric to quantify the changes occurring between two consecutive stepsof the process. This metric was tested on various generators as well as on real-world data, demonstratingits ability to capture the dynamics of a network, whether artificial or real. The study of these two conceptshas opened the door to many new questions and strengthened the connections between complex networkanalysis and temporal graph theory
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Saman, Nariman Goran. "A Framework for Secure Structural Adaptation." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78658.
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A (self-) adaptive system is a system that can dynamically adapt its behavior or structure during execution to "adapt" to changes to its environment or the system itself. From a security standpoint, there has been some research pertaining to (self-) adaptive systems in general but not enough care has been shown towards the adaptation itself. Security of systems can be reasoned about using threat models to discover security issues in the system. Essentially that entails abstracting away details not relevant to the security of the system in order to focus on the important aspects related to security. Threat models often enable us to reason about the security of a system quantitatively using security metrics. The structural adaptation process of a (self-) adaptive system occurs based on a reconfiguration plan, a set of steps to follow from the initial state (configuration) to the final state. Usually, the reconfiguration plan consists of multiple strategies for the structural adaptation process and each strategy consists of several steps steps with each step representing a specific configuration of the (self-) adaptive system. Different reconfiguration strategies have different security levels as each strategy consists of a different sequence configuration with different security levels. To the best of our knowledge, there exist no approaches which aim to guide the reconfiguration process in order to select the most secure available reconfiguration strategy, and the explicit security of the issues associated with the structural reconfiguration process itself has not been studied. In this work, based on an in-depth literature survey, we aim to propose several metrics to measure the security of configurations, reconfiguration strategies and reconfiguration plans based on graph-based threat models. Additionally, we have implemented a prototype to demonstrate our approach and automate the process. Finally, we have evaluated our approach based on a case study of our making. The preliminary results tend to expose certain security issues during the structural adaptation process and exhibit the effectiveness of our proposed metrics.
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Noharet, Léo, and Anton Fu. "Using simulated dynamics and graph metrics to compare brain networks of MCI patients and healthy control subjects." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301839.
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In recent years, several different methods have been proposed to compare brain networks with the joint use of graph theory and graph metrics. Another relatively unexplored comparison method is comparing the brain’s response to various input signals by simulating the brain’s dynamics. The brain activity signals are dependent on the physical connectivity patterns of the brain, therefore brain activity signals can be studied in addition to the study of connectivity patterns of brain graphs. Thus, in this study we use both static graph metrics and the dynamics of the brain to compare the brain networks of healthy test subjects and of patients with mild cognitive impairment (MCI). This was done by measuring and comparing the characteristic path length, clustering coefficient, small worldness and average degree distribution of each subject, as well as the Fourier transform amplitude at the frequency of the induced input signal. The result showed that the static graph metrics small worldness and clustering coefficient showed a small difference between the two groups. Differences between the two groups could also be observed with regards to the metric measured on the dynamics, however only between individual brain regions of the brain networks and only at certain input frequencies. The lack of consistency in these results hinders us from drawing deductions about this metric’s ability to differentiate the two groups. However, the study showed compelling enough results to argue for further research in comparing brain network with the use of simulated dynamics.<br>På senare år har flera olika metoder för att jämföra hjärnans nätverk framställts med hjälp av grafteori och olika statiska graf mätetal. En relativt outforskad jämförelsemetod är att jämföra påverkan som stimulanssignaler har på hjärnan med hjälp av att simulera hjärnans systemdynamik med stimulanssignaler av olika frekvenser som indata in i hjärnnätverket. Eftersom hjärnaktivitetssignaler är beroende av hjärnans struktur, kan man studera hjärnaktivitetssignaler tillsammans med hjärnans struktur. Följaktligen, ämnar denna studie att jämföra hjärnnätverken av patienter med mild kognitiv nedsättning (MCI) och av friska kontrolldeltagare genom att jämföra statiska graf mätetal och den påverkan som simulerade stimulanssignaler av varierande frekvenser har på hjärnan. Detta gjordes genom att jämföra graf mätetalen characteristic path length, clustering koefficient, small worldness och den genomsnittliga grad distributionen, samt att jämföra Fourier transform amplituden vid frekvensen av stimulanssignalen av varje deltagares hjärnnätverk vid sex olika tidpunkter. Studiens resultat visade på att de statiska graf mätetalen small worldness och clustering- koefficient kunde måttligen skilja grupperna. Skillnader mellan de två deltagargrupperna kunde även observeras vid vissa individuella hjärnregioner som utgör hjärnnätverken när Fourier transform amplituden vid stimulanssignalens frekvens användes som mätetal. Eftersom mätetalets resultater inte var homogena över hela datamängden, kan inte vi dra slutsatser om mätetalets förmåga att urskilja MCI patienter från friska kontrolldeltagare. Dock har studiens resultat visat tillräckligt övertalande tecken på att simuleringen av hjärnans systemdynamik med stimulanssignaler skulle kunne urskilja hjärnnätverk för att uppmana till vidare forskning kring denna jämförelsemetod.
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Hiransakolwong, Nualsawat. "AUTOMATIC ANNOTATION OF DATABASE IMAGES FOR QUERY-BY-CONCEPT." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2639.
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As digital images become ubiquitous in many applications, the need for efficient and effective retrieval techniques is more demanding than ever. Query by Example (QBE) and Query by Concept (QBC) are among the most popular query models. The former model accepts example images as queries and searches for similar ones based on low-level features such as colors and textures. The latter model allows queries to be expressed in the form of high-level semantics or concept words, such as "boat" or "car," and finds images that match the specified concepts. Recent research has focused on the connections between these two models and attempts to close the semantic-gap between them. This research involves finding the best method that maps a set of low-level features into high-level concepts. Automatic annotation techniques are investigated in this dissertation to facilitate QBC. In this approach, sets of training images are used to discover the relationship between low-level features and predetermined high-level concepts. The best mapping with respect to the training sets is proposed and used to analyze images, annotating them with the matched concept words. One principal difference between QBE and QBC is that, while similarity matching in QBE must be done at the query time, QBC performs concept exploration off-line. This difference allows QBC techniques to shift the time-consuming task of determining similarity away from the query time, thus facilitating the additional processing time required for increasingly accurate matching. Consequently, QBC's primary design objective is to achieve accurate annotation within a reasonable processing time. This objective is the guiding principle in the design of the following proposed methods which facilitate image annotation: 1.A novel dynamic similarity function. This technique allows users to query with multiple examples: relevant, irrelevant or neutral. It uses the range distance in each group to automatically determine weights in the distance function. Among the advantages of this technique are higher precision and recall rates with fast matching time. 2.Object recognition based on skeletal graphs. The topologies of objects' skeletal graphs are captured and compared at the node level. Such graph representation allows preservation of the skeletal graph's coherence without sacrificing the flexibility of matching similar portions of graphs across different levels. The technique is robust to translation, scaling, and rotation invariants at object level. This technique achieves high precision and recall rates with reasonable matching time and storage space. 3.ASIA (Automatic Sampling-based Image Annotation) is a technique based on a new sampling-based matching framework allowing users to identify their area of interest. ASIA eliminates noise, or irrelevant areas of the image. ASIA is robust to translation, scaling, and rotation invariants at the object level. This technique also achieves high precision and recall rates. While the above techniques may not be the fastest when contrasted with some other recent QBE techniques, they very effectively perform image annotation. The results of applying these processes are accurately annotated database images to which QBC may then be applied. The results of extensive experiments are presented to substantiate the performance advantages of the proposed techniques and allow them to be compared with other recent high-performance techniques. Additionally, a discussion on merging the proposed techniques into a highly effective annotation system is also detailed.<br>Ph.D.<br>School of Computer Science<br>Engineering and Computer Science<br>Computer Science
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Zhao, Z. W., and I.-Ming Chen. "Optimizing the Dynamic Distribution of Data-stream for High Speed Communications." 2004. http://hdl.handle.net/1721.1/7459.
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The performances of high-speed network communications frequently rest with the distribution of data-stream. In this paper, a dynamic data-stream balancing architecture based on link information is introduced and discussed firstly. Then the algorithms for simultaneously acquiring the passing nodes and links of a path between any two source-destination nodes rapidly, as well as a dynamic data-stream distribution planning are proposed. Some related topics such as data fragment disposal, fair service, etc. are further studied and discussed. Besides, the performance and efficiency of proposed algorithms, especially for fair service and convergence, are evaluated through a demonstration with regard to the rate of bandwidth utilization. Hoping the discussion presented here can be helpful to application developers in selecting an effective strategy for planning the distribution of data-stream.<br>Singapore-MIT Alliance (SMA)
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Jasinski, Jakub. "Hrushovski and Ramsey Properties of Classes of Finite Inner Product Structures, Finite Euclidean Metric Spaces, and Boron Trees." Thesis, 2011. http://hdl.handle.net/1807/29762.
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We investigate two combinatorial properties of classes of finite structures, as well as related applications to topological dynamics. Using the Hrushovski property of classes of finite structures -- a finite extension property of homomorphisms -- we can show the existence of ample generics. For example, Solecki proved the existence of ample generics in the context of finite metric spaces that do indeed possess this extension property. Furthermore, Kechris, Pestov and Todorcevic have shown that the Ramsey property of Fraisse classes of finite structures implies that the automorphism group of the corresponding Fraisse limit is extremely amenable, i.e., it possesses a very strong fixed point property.
Gromov and Milman had shown that the unitary group of the infinite-dimensional separable Hilbert space is extremely amenable using non-combinatorial methods. This result encourages a deeper look into structural Euclidean Ramsey theory, i.e., Euclidean Ramsey theory in which we colour more than just points. In particular, we look at complete finite labeled graphs whose vertex sets are subsets of the Hilbert space and whose labels correspond to the inner products. We prove "Ramsey-type" and "Hrushovski-type" theorems for linearly ordered metric subspaces of "sufficiently" orthogonal sets. In particular, the latter is used to show a "Hrushovski version" of the Ramsey-type Matousek-Rodl theorem for simplices.
It is known that the square root of the metric induced by the distance between vertices in graphs produces a metric space embeddable in a Euclidean space if and only if the graph is a metric subgraph of the Cartesian product of three types of graphs. These three are the half-cube graphs, the so-called cocktail party graphs, and the Gosset graph. We show that the class of metric spaces related to half-cube graphs -- metric spaces on sets with the symmetric difference metric -- satisfies the Hrushovski property up to 3 points, but not more. Moreover, the amalgamation in this class can be too restrictive to permit the Ramsey Property.
Finally, following the work of Fouche, we compute the Ramsey degrees of structures induced by the leaf sets of boron trees. Also, we briefly show that this class does not satisfy the full Hrushovski property. Fouche's trees are in fact related to ultrametric spaces, as was observed by Lionel Nguyen van The. We augment Fouche's concept of orientation so that it applies to these boron tree structures. The upper bound computation of the Ramsey degree in this case, turns out to be an "asymmetric" version of the Graham-Rothschild theorem. Finally, we extend these structures to "oriented" ones, yielding a Ramsey class and a corresponding Fraisse limit whose automorphism group is extremely amenable.
Books on the topic "Dynamic Graph Metrics"
Book chapters on the topic "Dynamic Graph Metrics"
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Meidiana, Amyra, Seok-Hee Hong, and Peter Eades. "New Quality Metrics for Dynamic Graph Drawing." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-68766-3_35.
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Gain, Ayan, and Mridul Sankar Barik. "Attack Graph Based Security Metrics for Dynamic Networks." In Information Systems Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49099-6_7.
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Vincent, Bridonneau, Guinand Frédéric, and Pigné Yoann. "DynamicScore: A Novel Metric for Quantifying Graph Dynamics." In Complex Networks & Their Applications XII. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53499-7_35.
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O’ Mahony, Niall, Anshul Awasthi, Joseph Walsh, and Daniel Riordan. "Latent Space Cartography for Geometrically Enriched Latent Spaces." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_38.
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AbstractThere have been many developments in recent years on the exploitation of non-Euclidean geometry for the better representation of the relation between subgroups in datasets. Great progress has been made in this field of Disentangled Representation Learning, in leveraging information geometry divergence, manifold regularisation and geodesics to allow complex dynamics to be captured in the latent space of the representations produced. However, interpreting the high-dimensional latent spaces of the modern deep learning-based models involved is non-trivial. Therefore, in this paper, we investigate how techniques in Latent Space Cartography can be used to display abstract and representational 2D visualisations of manifolds.Additionally, we present a multi-task metric learning model to capture in its output representations as many metrics as is available in a multi-faceted fine-grained change detection dataset. We also implement an interactive visualisation tool that utilises cartographic techniques that allow dimensions and annotations of graphs to be representative of the underlying factors affecting individual scenarios the user can morph and transform to focus on an individual/sub-group to see how they are performing with respect to said metrics.
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Kim, Woojin, Facundo Mémoli, and Zane Smith. "Analysis of Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence." In Topological Data Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43408-3_14.
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Burattin, Andrea, Hugo A. López, and Lasse Starklit. "Uncovering Change: A Streaming Approach for Declarative Processes." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_12.
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AbstractProcess discovery is a family of techniques that helps to comprehend processes from their data footprints. Yet, as processes change over time so should their corresponding models, and failure to do so will lead to models that under- or over-approximate behaviour. We present a discovery algorithm that extracts declarative processes as Dynamic Condition Response (DCR) graphs from event streams. Streams are monitored to generate temporal representations of the process, later processed to create declarative models. We validated the technique by identifying drifts in a publicly available dataset of event streams. The metrics extend the Jaccard similarity measure to account for process change in a declarative setting. The technique and the data used for testing are available online.
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Wang, Di. "Explainable Deep Reinforcement Learning for Knowledge Graph Reasoning." In Recent Developments in Machine and Human Intelligence. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9189-8.ch012.
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Artificial intelligence faces a considerable challenge in automated reasoning, particularly in inferring missing data from existing observations. Knowledge graph (KG) reasoning can significantly enhance the performance of context-aware AI systems such as GPT. Deep reinforcement learning (DRL), an influential framework for sequential decision-making, exhibits strength in managing uncertain and dynamic environments. Definitions of state space, action space, and reward function in DRL directly dictate the performances. This chapter provides an overview of the pipeline and advantages of leveraging DRL for knowledge graph reasoning. It delves deep into the challenges of KG reasoning and features of existing studies. This chapter offers a comparative study of widely used state spaces, action spaces, reward functions, and neural networks. Furthermore, it evaluates the pros and cons of DRL-based methodologies and compares the performances of nine benchmark models across six unique datasets and four evaluation metrics.
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Beynier, Aurélie, Jean-Guy Mailly, Nicolas Maudet, and Anaëlle Wilczynski. "Explaining the Lack of Locally Envy-Free Allocations." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240900.
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In fair division, local envy-freeness is a desirable property which has been thoroughly studied in recent years. In this paper, we study explanations which can be given to explain that no allocation of items can satisfy this criterion, in the house allocation setting where agents receive a single item. While Minimal Unsatisfiable Subsets (MUSes) are key concepts to extract explanations, they cannot be used as such: (i) they highly depend on the initial encoding of the problem; (ii) they are flat structures which fall short of capturing the dynamics of explanations; (iii) they typically come in large number and exhibit great diversity. In this paper we provide two SAT encodings of the problem which allow us to extract MUS when instances are unsatisfiable. We build a dynamic graph structure which allows to follow step-by-step the explanation. Finally, we propose several criteria to select MUSes, some of them being based on the MUS structure, while others rely on this original graphical explanation structure. We give theoretical bounds on these metrics, showing that they can vary significantly for some instances. Experimental results on synthetic data complement these results and illustrate the impact of the encodings and the relevance of our metrics to select among the many MUSes.
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Marmier, Arnaud. "Anomalous Mechanical Behaviour Arising From Framework Flexibility." In Mechanical Behaviour of Metal – Organic Framework Materials. The Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781839166594-00065.
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This chapter describes in detail the framework of anisotropic elasticity to introduce exotic elastic and thermoelastic properties that are often prevalent and extreme in MOFs: negative linear compressibility (NLC), negative thermal expansion (NTE) and negative Poisson's ratio (NPR). The complete elastic tensors of 56 MOFs are collated and analysed to reveal that NPR is very common in non-cubic MOF structures, but the mechanisms are not yet clear. This is in contrast to NLC and NTE, which derive from the interplay between rigid, stiff building blocks and compliant joint flexing in ‘wine-rack’ type motions and dynamic equivalents. As the existence of flexible mechanisms seems to allow large and unusual framework deformations, several potential measures of materials and framework flexibility are considered, from ad hoc elastic anisotropy metrics to graph-theoretical and group-theoretical analyses, including physical models.
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Tauch, Sotharith, William Liu, and Russel Pears. "Measuring Cascading Failures in Smart Grid Networks." In Smart Grid as a Solution for Renewable and Efficient Energy. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0072-8.ch009.
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Smart Grid is the next generation of the electrical power systems that form from interdependent networks. Cascading failures in such interdependent critical infrastructure is very crucial which can cause wide spread disruption. This chapter is intended to evaluate four different topological metrics in which can be best described and approximated the behavior of cascading failures in interdependent networks by employing two interlinks strategies such as random interlinks addition and degree-degree correlation interlinks. The four chosen topological metrics are algebraic connectivity, effective graph resistance, average betweenness centrality, and average distance. Throughout the chapter, analytical study of each metric are discussed and also compared with numerical simulation based on sandpile dynamics load distribution.
Conference papers on the topic "Dynamic Graph Metrics"
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Cai, Borui, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, and Jianxin Li. "Temporal Knowledge Graph Completion: A Survey." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/734.
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Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Emerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the preliminary knowledge, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how the temporal validity of facts is used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.
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Marsili, Valentina, Stefano Alvisi, Filomena Maietta, et al. "Characterizing the effects of water distribution system topology modifications on its dynamic behaviour through connectivity metrics." In 2nd WDSA/CCWI Joint Conference. Editorial Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/wdsa-ccwi2022.2022.14016.
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Water distribution networks (WDNs) are complex combinations of nodes and links and their structure has an impact on their behaviour, considering both quantitative (i.e. related to pipe flows and nodal pressures) and qualitative (i.e. related to water age and quality) aspects. The complexity of WDNs has been the basis of several studies that have resorted to the graph theory to relate connectivity properties to system behaviour (e.g. its reliability and water age/quality), evaluated under the assumption of steady-state conditions. Within this framework, in recent years the tendency toward reducing network interconnection through the closure of isolation valves has emerged, mainly to (i) facilitate its monitoring and management, and (ii) increase flow velocity and reduce water age. However, changes in the topology of a network can affect not only aspects evaluated under the assumption of steady-state conditions, but also its dynamic behaviour. Based on these considerations, the present study investigates whether some metrics derived from graph theory, already applied in the context of networks’ steady-state analyses, can also provide useful indications for assessing the effects of changes in the topological structure, which could be consequences of branching operations, on the dynamic response of a network subjected to users’ activity. The analyses highlight that connectivity metrics can reflect the pressure dynamic behaviour of the hydraulic systems and support in their macroscopic understanding during design and management operations. Thus, their application can be effectively extended from the steady-state to the dynamic framework.
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Kotecha, Maulik C., Wanyu Xu, and Daniel A. McAdams. "Exploring a Graph Theoretic Approach for the Quantitative Comparison of Digital Twin Designs." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-142928.
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Abstract A graph theoretic approach for quantitatively comparing digital twin designs is explored in this paper, with a novel methodology being offered to evaluate the structural and functional similarities between these complex systems. Graph theory is leveraged to represent digital twins as mathematical graphs in our approach, with nodes represented by function-flow pairs and the interactions between the functions depicted by edges. The application of graph-based similarity measures, such as NetSimile and DeltaCon distance, for quantitatively assessing the resemblance between digital twin designs is allowed by this representation. Our method addresses the limitations of conventional vector-based metrics of design comparison, which focuses on the connectivity and interactions within these systems, offering a more nuanced and comprehensive analysis. The designs of six digital twins were collected, and functional models were created using the Functional Basis. The functional models were then converted to graphs to perform design similarity analysis. At the end of the study, it was concluded that graph similarity measures such as NetSimile and DeltaCon can be used as quantitative tools for comparing digital twins. However, further investigation is needed to test the effectiveness of such measures for complex designs such as digital twins. The limitations inherent in the current methodology are highlighted, such as challenges in capturing dynamic interactions and the evolving nature of digital twins. Finally, graph neural networks (GNN) are proposed for multiple feature-based similarity analysis and large language models (LLMs) to automate functional model generation.
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Zhao, Sheng, Baisravan HomChaudhuri, and Manish Kumar. "A Method for Distributed Optimization for Task Allocation." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2694.
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Allocation of a large number of resources to tasks in a complex environment is often a very challenging problem. This is primarily due to the fact that a large number of resources to be allocated results into an optimization problem that involves a large number of decision variables. Most of the optimization algorithms suffer from this issue of non-scalability. Further, the uncertainties and dynamic nature of environment make the optimization problem quite challenging. One of the techniques to overcome the issue of scalability that have been considered recently is to carry out the optimization in a distributed or decentralized manner. Such techniques make use of local information to carry out global optimization. However, such techniques tend to get stuck in local minima. Further, the connectivity graph that governs the sharing of information plays a role in the performance of algorithms in terms of time taken to obtain the solution, and quality of the solution with respect to the global solution. In this paper, we propose a distributed greedy algorithm inspired by market based concepts to optimize a cost function. This paper studies the effectiveness of the proposed distributed algorithm in obtaining global solutions and the phase transition phenomenon with regard to the connectivity metrics of the graph that underlies the network of information exchange. A case study involving resource allocation in wildland firefighting is provided to demonstrate our algorithm.
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Morozov, Andrey, Mihai A. Diaconeasa, and Mikael Steurer. "A Hybrid Methodology for Model-Based Probabilistic Resilience Evaluation of Dynamic Systems." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23789.
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Abstract Advanced classical Probabilistic Risk Assessment (PRA) effectively combines various methods for quantitative risk evaluation, such as event trees, fault trees, and Bayesian networks. PRA methods and tools provide the means for the qualitative reliability evaluation (e.g., cut sets) and the computation of quantitative reliability metrics (e.g., end states probabilities). Modern safety-critical systems from various industrial domains tend toward a high level of autonomy and demand not only reliability but also resilience, the ability to recover from degraded or failed states. The numerical resilience analysis of such dynamic systems requires more flexible methods. These methods shall enable the analysis of the systems with sophisticated software parts and dynamic feedback loops. A suitable candidate is the Dual-graph Error Propagation Model (DEPM) that can capture nontrivial failure scenarios and dynamic fault-tolerance mechanisms. The DEPM exploits the method for the automatic generation of Markov chain models and the application of probabilistic model checking techniques. Moreover, the DEPM enables the analysis of highly-customizable system resilience metrics, e.g., “the probability of system recovery to a particular state after a specified system failure during a defined time interval.” In this paper, we show how DEPM-based resilience analysis can be integrated with the general PRA methodology for resilience evaluations. The proposed methodology is demonstrated on a safety-critical autonomous UAV system.
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DELO, GIULIA, CECILIA SURACE, KEITH WORDEN, and DANIEL S. BRENNAN. "ON THE INFLUENCE OF STRUCTURAL ATTRIBUTES FOR ASSESSING SIMILARITY IN POPULATION-BASED STRUCTURAL HEALTH MONITORING." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36904.
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The viability of many machine learning methods within Structural Health Monitoring (SHM) is often limited by the lack, or the incompleteness, of the data required for implementing these algorithms. Indeed, learning a data-based SHM predictive model usually requires the dynamic response availability for undamaged and damaged states, and the assumption that both training and test data refer to the same domain. In this framework, the population-based approach to Structural Health Monitoring (PBSHM) aims at improving the performance and the robustness of diagnostic inferences, exploiting the transfer of damage-state knowledge across a population of structures. However, sharing these data produces a meaningful inference only if the structures, and their datasets, are sufficiently similar. Therefore, an initial phase of similarity assessment becomes essential before being able to apply transfer learning algorithms. This phase shows which structures are suitable for knowledge sharing, if any, reducing the possibility of negative transfer. Some distance metrics have been proposed, exploiting abstract representations of structures, such as Irreducible Element (IE) models and Attributed Graphs (AGs). Although these metrics can consider the structure attributes, many performed comparisons mainly concern structural topology. This study aims at broadening the application of similarity assessment, focussing on the geometrical and material differences in the distance metrics. Therefore, a heterogeneous population of laboratory-scale aircraft is analysed. These structures predominantly follow the geometry of a benchmark study conducted by the Structures and Materials Action Group (SM-AG19) of the Group for Aeronautical Research Technology in EURope (GARTEUR). The IE models of these aircraft are produced. Subsequently, Graph Matching Network (GMNs) are used to determine the similarity matrix. The structures in the Garteur population are topologically homogeneous, which enables a more accurate investigation of how attributes can influence distance metrics. This paper constitutes the first step in the Garteur structures population investigation. The similarity assessment results will establish which population members are most suitable for applying transfer learning algorithms. It will enable the subsequent development, and experimental validation, of a population-based strategy for damage identification across heterogeneous structures.
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Popov, Anton I., Igor Y. Popov, Dmitri S. Nikiforov, and Irina V. Blinova. "Time-dependent metric graph: Wave dynamics." In CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5114299.
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Zheng, Xiaolin, Jiajie Su, Weiming Liu, and Chaochao Chen. "DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation." In MM '22: The 30th ACM International Conference on Multimedia. ACM, 2022. http://dx.doi.org/10.1145/3503161.3548072.
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Louca, Loucas S., and Evagoras Xydas. "Model Reduction of Modal Representations." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14097.
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Dynamic analysis is extensively used to study the behavior of continuous and lumped parameter linear systems. In addition to the physical space, analyses can also be performed in the modal space where useful frequency information of the system can be extracted. More specifically, modal analysis can be used for the analysis and controller design of dynamic systems, where reduction of model complexity without degrading the accuracy is often required for the efficient use of the model. The reduction of modal models has been extensively studied and many reduction techniques are available. The majority of these techniques use frequency as the metric to determine the reduced model. This paper describes a new method for calculating the modal power of lumped parameter systems with the use of the bond graph representation, which is developed through a power conserving modal decomposition. This method is then used to reduce the size of the model. This technique is based on the previously developed Model Order Reduction Algorithm (MORA), which uses an energy-based metric to generate a series of proper reduced models. An example is provided to demonstrate the calculation of the modal power and the elimination of unimportant modes or modal elements using MORA.
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McCain, B. A., and A. G. Stefanopoulou. "Order Reduction for a Control-Oriented Model of the Water Dynamics in Fuel Cells." In ASME 2006 4th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2006. http://dx.doi.org/10.1115/fuelcell2006-97075.
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Predicting the water dynamics and estimating humidity and flooding conditions in a low-temperature fuel cell are critical for robust operation and long life. Previous work by McKay et al [1] shows that the fuel cell anode, cathode, and membrane water dynamics and gaseous species concentrations can be accurately modeled by discretizing the partial differential equations that describe mass transport into three segments. Avoiding sensitivities associated with over-parameterization, and allowing for the real-time computations necessary for embedded controllers, requires in-depth investigation of the model order. In this paper the model from [1] is formulated into a bond graph representation. The objective is to establish the necessary model order for the fuel cell model using the Model Order Reduction Algorithm (MORA) [2], where an energy-based metric termed the Activity is used to quantify the contribution of each element of the model. Activity is a scalar quantity that is determined from the generalized effort and flow through each element of the model. We show the degree of model order reduction and provide a guideline for appropriate discretization.