Relevant bibliographies by topics / Dynamic Graph Generation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Dynamic Graph Generation'

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Published: 25 January 2025

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Journal articles on the topic "Dynamic Graph Generation"

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Fan, Shaohua, Shuyang Zhang, Xiao Wang, and Chuan Shi. "Directed Acyclic Graph Structure Learning from Dynamic Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7512–21. http://dx.doi.org/10.1609/aaai.v37i6.25913.

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Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge.
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Ke, Qingchao, and Jian Lin. "Dynamic Generation of Knowledge Graph Supporting STEAM Learning Theme Design." Applied Sciences 12, no. 21 (October 30, 2022): 11001. http://dx.doi.org/10.3390/app122111001.

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Instructional framework based on a knowledge graph makes up for the interdisciplinary theme design ability of teachers in a single discipline, to some extent, and provides a curriculum-oriented theme generation path for STEAM instructional design. This study proposed a dynamic completion model of a knowledge graph based on the subject semantic tensor decomposition. This model can be based on the tensor calculation of multi-disciplinary curriculum standard knowledge semantics to provide more reasonable STEAM project-based learning themes for teachers of those subjects. First, the STEAM multi-disciplinary knowledge semantic dataset was generated through the course’s standard text and open-source encyclopedia data. Next, based on the semantic tensor decomposition of specific STEAM topics, the dynamic generation of knowledge graphs was realized, providing interdisciplinary STEAM learning topic sequences for teachers of a single discipline. Finally, the application experiment of generating STEAM learning themes proved the effectiveness of our model.
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Chen, Libin, Luyao Wang, Chengyi Zeng, Hongfu Liu, and Jing Chen. "DHGEEP: A Dynamic Heterogeneous Graph-Embedding Method for Evolutionary Prediction." Mathematics 10, no. 22 (November 9, 2022): 4193. http://dx.doi.org/10.3390/math10224193.

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Current graph-embedding methods mainly focus on static homogeneous graphs, where the entity type is the same and the topology is fixed. However, in real networks, such as academic networks and shopping networks, there are typically various types of nodes and temporal interactions. The dynamical and heterogeneous components of graphs in general contain abundant information. Currently, most studies on dynamic graphs do not sufficiently consider the heterogeneity of the network in question, and hence the semantic information of the interactions between heterogeneous nodes is missing in the graph embeddings. On the other hand, the overall size of the network tends to accumulate over time, and its growth rate can reflect the ability of the entire network to generate interactions of heterogeneous nodes; therefore, we developed a graph dynamics model to model the evolution of graph dynamics. Moreover, the temporal properties of nodes regularly affect the generation of temporal interaction events with which they are connected. Thus, we developed a node dynamics model to model the evolution of node connectivity. In this paper, we propose DHGEEP, a dynamic heterogeneous graph-embedding method based on the Hawkes process, to predict the evolution of dynamic heterogeneous networks. The model considers the generation of temporal events as an effect of historical events, introduces the Hawkes process to simulate this evolution, and then captures semantic and structural information based on the meta-paths of temporal heterogeneous nodes. Finally, the graph-level dynamics of the network and the node-level dynamics of each node are integrated into the DHGEEP framework. The embeddings of the nodes are automatically obtained by minimizing the value of the loss function. Experiments were conducted on three downstream tasks, static link prediction, temporal event prediction for homogeneous nodes, and temporal event prediction for heterogeneous nodes, on three datasets. Experimental results show that DHGEEP achieves excellent performance in these tasks. In the most significant task, temporal event prediction of heterogeneous nodes, the values of precision@2 and recall@2 can reach 30.23% and 10.48% on the AMiner dataset, and reach 4.56% and 1.61% on the DBLP dataset, so that our method is more accurate at predicting future temporal events than the baseline.
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Yang, Yu, An Wang, Hua Wang, Wei-Ting Zhao, and Dao-Qiang Sun. "On Subtrees of Fan Graphs, Wheel Graphs, and “Partitions” of Wheel Graphs under Dynamic Evolution." Mathematics 7, no. 5 (May 24, 2019): 472. http://dx.doi.org/10.3390/math7050472.

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The number of subtrees, or simply the subtree number, is one of the most studied counting-based graph invariants that has applications in many interdisciplinary fields such as phylogenetic reconstruction. Motivated from the study of graph surgeries on evolutionary dynamics, we consider the subtree problems of fan graphs, wheel graphs, and the class of graphs obtained from “partitioning” wheel graphs under dynamic evolution. The enumeration of these subtree numbers is done through the so-called subtree generation functions of graphs. With the enumerative result, we briefly explore the extremal problems in the corresponding class of graphs. Some interesting observations on the behavior of the subtree number are also presented.
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Singh, Priyank Kumar, Sami Ur Rehman, Darshan J, Shobha G, and Deepamala N. "Automated dynamic schema generation using knowledge graph." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (December 1, 2022): 1261. http://dx.doi.org/10.11591/ijai.v11.i4.pp1261-1269.

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<span>On the internet where the number of database developers is increasing with the availability of huge data to be stored and queried. Establishing relations between various schemas and helping the developers by filtering, prioritizing, and suggesting relevant schema is a requirement. Recommendation system plays an important role in searching through a large volume of dynamically generated schemas to provide database developers with personalized schemas and services. Although many methods are already available to solve problems using machine learning, they require more time and data to learn. These problems can be solved using knowledge graphs (KG). This paper investigates building knowledge graphs to recommend schemas. </span>
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Kumari, Kabita, and Hashim Zahoor. "SmartGraphAI: Real Time Graph Generation with AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (November 27, 2024): 1–8. http://dx.doi.org/10.55041/ijsrem39110.

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SmartGraphAI, an innovative web platform, is designed to revolutionize the way users generate graphs in real-time using AI and large language models (LLMs). By processing natural language queries, SmartGraphAI intelligently breaks down user requests into multiple sub-queries that retrieve relevant data from the internet. This data is then integrated into user-friendly visualizations through dynamic graphs that adapt to the user’s requirements. The platform leverages advanced LLM technology for query interpretation and utilizes web scraping APIs for real-time data extraction, ensuring that users receive up-to-date, accurate insights. The backend, built on FastAPI, manages the data flow seamlessly, while the frontend, developed with React, provides a smooth and intuitive interface for users to engage with. SmartGraphAI empowers professionals across industries to quickly transform complex data into meaningful visual insights, automating the entire process from query input to graph generation, and thus, enhancing decision-making and efficiency in data analysis. Keywords AI, Graph Generation, Real-Time, Large Language Models, Data Visualization, Web Scraping
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Shen, Yanyan, Lei Chen, Jingzhi Fang, Xin Zhang, Shihong Gao, and Hongbo Yin. "Efficient Training of Graph Neural Networks on Large Graphs." Proceedings of the VLDB Endowment 17, no. 12 (August 2024): 4237–40. http://dx.doi.org/10.14778/3685800.3685844.

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Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data. Mainstream GNNs employ the message passing scheme that iteratively propagates information between connected nodes through edges. However, this scheme incurs high training costs, hindering the applicability of GNNs on large graphs. Recently, the database community has extensively researched effective solutions to facilitate efficient GNN training on massive graphs. In this tutorial, we provide a comprehensive overview of the GNN training process based on the graph data lifecycle, covering graph preprocessing, batch generation, data transfer, and model training stages. We discuss recent data management efforts aiming at accelerating individual stages or improving the overall training efficiency. Recognizing the distinct training issues associated with static and dynamic graphs, we first focus on efficient GNN training on static graphs, followed by an exploration of training GNNs on dynamic graphs. Finally, we suggest some potential research directions in this area. We believe this tutorial is valuable for researchers and practitioners to understand the bottleneck of GNN training and the advanced data management techniques to accelerate the training of different GNNs on massive graphs in diverse hardware settings.
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Chen, I.-Ming, and Guilin Yang. "Automatic Model Generation for Modular Reconfigurable Robot Dynamics." Journal of Dynamic Systems, Measurement, and Control 120, no. 3 (September 1, 1998): 346–52. http://dx.doi.org/10.1115/1.2805408.

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In control and simulation of a modular robot system, which consists of standardized and interconnected joint and link units, manual derivation of its dynamic model needs tremendous effort because these models change all the time as the robot geometry is altered after module reconfiguration. This paper presents a method to automate the generation of the closed-form equation of motion of a modular robot with arbitrary degrees-of-freedom and geometry. The robot geometry we consider here is branching type without loops. A graph technique, termed kinematic graphs and realized through assembly incidence matrices (AIM) is introduced to represent the module assembly sequence and robot geometry. The formulation of the dynamic model is started with recursive Newton-Euler algorithm. The generalized velocity, acceleration, and forces are expressed in terms of linear operations on se(3), the Lie algebra of the Euclidean group SE(3). Based on the equivalence relationship between the recursive formulation and the closed-form Lagrangian formulation, the accessibility matrix of the kinematic graph of the robot is used to assist the construction of the closed-form equation of motion of a modular robot. This automatic model generation technique can be applied to the control of rapidly reconfigurable robotic workcells and other automation equipment built around modular components that require accurate dynamic models.
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Chen, Jin, Xiaofeng Ji, and Xinxiao Wu. "Adaptive Image-to-Video Scene Graph Generation via Knowledge Reasoning and Adversarial Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 276–84. http://dx.doi.org/10.1609/aaai.v36i1.19903.

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Scene graph in a video conveys a wealth of information about objects and their relationships in the scene, thus benefiting many downstream tasks such as video captioning and visual question answering. Existing methods of scene graph generation require large-scale training videos annotated with objects and relationships in each frame to learn a powerful model. However, such comprehensive annotation is time-consuming and labor-intensive. On the other hand, it is much easier and less cost to annotate images with scene graphs, so we investigate leveraging annotated images to facilitate training a scene graph generation model for unannotated videos, namely image-to-video scene graph generation. This task presents two challenges: 1) infer unseen dynamic relationships in videos from static relationships in images due to the absence of motion information in images; 2) adapt objects and static relationships from images to video frames due to the domain shift between them. To address the first challenge, we exploit external commonsense knowledge to infer the unseen dynamic relationship from the temporal evolution of static relationships. We tackle the second challenge by hierarchical adversarial learning to reduce the data distribution discrepancy between images and video frames. Extensive experiment results on two benchmark video datasets demonstrate the effectiveness of our method.
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Maghawry, Noura, Samy Ghoniemy, Eman Shaaban, and Karim Emara. "An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case." Big Data and Cognitive Computing 7, no. 1 (January 24, 2023): 21. http://dx.doi.org/10.3390/bdcc7010021.

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Semantic data integration provides the ability to interrelate and analyze information from multiple heterogeneous resources. With the growing complexity of medical ontologies and the big data generated from different resources, there is a need for integrating medical ontologies and finding relationships between distinct concepts from different ontologies where these concepts have logical medical relationships. Standardized Medical Ontologies are explicit specifications of shared conceptualization, which provide predefined medical vocabulary that serves as a stable conceptual interface to medical data sources. Intelligent Healthcare systems such as disease prediction systems require a reliable knowledge base that is based on Standardized medical ontologies. Knowledge graphs have emerged as a powerful dynamic representation of a knowledge base. In this paper, a framework is proposed for automatic knowledge graph generation integrating two medical standardized ontologies- Human Disease Ontology (DO), and Symptom Ontology (SYMP) using a medical online website and encyclopedia. The framework and methodologies adopted for automatically generating this knowledge graph fully integrated the two standardized ontologies. The graph is dynamic, scalable, easily reproducible, reliable, and practically efficient. A subgraph for cancer terms is also extracted and studied for modeling and representing cancer diseases, their symptoms, prevention, and risk factors.
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Dissertations / Theses on the topic "Dynamic Graph Generation"

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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
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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Pogulis, Jakob. "Generation of dynamic control-dependence graphs for binary programs." Thesis, Linköpings universitet, Databas och informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110247.

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Dynamic analysis of binary files is an area of computer science that has many purposes. It is useful when it comes to debugging software in a development environment and the developer needs to know which statements affected the value of a specific variable. But it is also useful when analyzing a software for potential vulnerabilities, where data controlled by a malicious user could potentially result in the software executing adverse commands or executing malicious code. In this thesis a tool has been developed to perform dynamic analysis of x86 binaries in order to generate dynamic control-dependence graphs over the execution. These graphs can be used to determine which conditional statements that resulted in a certain outcome. The tool has been developed for x86 Linux systems using the dynamic binary instrumentation framework PIN, developed and maintained by Intel. Techniques for utilizing the additional information about the control flow for a program available during the dynamic analysis in order to improve the control flow information have been implemented and tested. The basic theory of dynamic analysis as well as dynamic slicing is discussed, and a basic overview of the implementation of a dynamic analysis tool is presented. The impact on the performance of the dynamic analysis tool for the techniques used to improve the control flow graph is significant, but approaches to improving the performance are discussed.
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Fischer, Frank. "Dynamic Graph Generation and an Asynchronous Parallel Bundle Method Motivated by Train Timetabling." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-118358.

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Lagrangian relaxation is a successful solution approach for many combinatorial optimisation problems, one of them being the train timetabling problem (TTP). We model this problem using time expanded networks for the single train schedules and coupling constraints to enforce restrictions like station capacities and headway times. Lagrangian relaxation of these coupling constraints leads to shortest path subproblems in the time expanded networks and is solved using a proximal bundle method. However, large instances of our practical partner Deutsche Bahn lead to computationally intractable models. In this thesis we develop two new algorithmic techniques to improve the solution process for this kind of optimisation problems. The first new technique, Dynamic Graph Generation (DGG), aims at improving the computation of the shortest path subproblems in large time expanded networks. Without sacrificing any accuracy, DGG allows to store only small parts of the networks and to dynamically extend them whenever the stored part proves to be too small. This is possible by exploiting the properties of the objective function in many scheduling applications to prefer early paths or due times, respectively. We prove that DGG can be implemented very efficiently and its running time and the size of nodes that have to be stored additionally does not depend on the size of the time expanded network but only on the length of the train routes. The second technique is an asynchronous and parallel bundle method (APBM). Traditional bundle methods require one solution of each subproblem in each iteration. However, many practical applications, e.g. the TTP, consist of rather loosely coupled subproblems. The APBM chooses only small subspaces corresponding to the Lagrange multipliers of strongly violated coupling constraints and optimises only these variables while keeping all other variables fixed. Several subspaces of disjoint variables may be chosen simultaneously and are optimised in parallel. The solutions of the subspace problem are incorporated into the global data as soon as it is available without any synchronisation mechanism. However, in order to guarantee convergence, the algorithm detects automatically dependencies between different subspaces and respects these dependencies in future subspace selections. We prove the convergence of the APBM under reasonable assumptions for both, the dual and associated primal aggregate data. The APBM is then further extended to problems with unknown dependencies between subproblems and constraints in the Lagrangian relaxation problem. The algorithm automatically detects these dependencies and respects them in future iterations. Again we prove the convergence of this algorithm under reasonable assumptions. Finally we test our solution approach for the TTP on some real world instances of Deutsche Bahn. Using an iterative rounding heuristic based on the approximate fractional solutions obtained by the Lagrangian relaxation we are able to compute feasible schedules for all trains in a subnetwork of about 10% of the whole German network in about 12 hours. In these timetables 99% of all passenger trains could be scheduled with no significant delay and the travel time of the freight trains could be reduced by about one hour on average.
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Fischer, Frank [Verfasser], Christoph [Akademischer Betreuer] Helmberg, and Marco [Gutachter] Lübbecke. "Dynamic Graph Generation and an Asynchronous Parallel Bundle Method Motivated by Train Timetabling / Frank Fischer ; Gutachter: Marco Lübbecke ; Betreuer: Christoph Helmberg." Chemnitz : Universitätsbibliothek Chemnitz, 2013. http://d-nb.info/1214245811/34.

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Zhu, Xiaoyan. "The dynamic, resource-constrained shortest path problem on an acyclic graph with application in column generation and literature review on sequence-dependent scheduling." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4996.

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This dissertation discusses two independent topics: a resource-constrained shortest-path problem (RCSP) and a literature review on scheduling problems involving sequence-dependent setup (SDS) times (costs). RCSP is often used as a subproblem in column generation because it can be used to solve many practical problems. This dissertation studies RCSP with multiple resource constraints on an acyclic graph, because many applications involve this configuration, especially in column genetation formulations. In particular, this research focuses on a dynamic RCSP since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-time “preliminary” phase to transform RCSP into an unconstrained shortest path problem (SPP) and then solves the resulting SPP after new values of dual variables are used to update objective function coefficients (i.e., reduced costs) at each iteration. Network reduction techniques are considered to remove some nodes and/or arcs permanently in the preliminary phase. Specified techniques are explored to reoptimize when only several coefficients change and for dealing with forbidden and prescribed arcs in the context of a column generation/branch-and-bound approach. As a benchmark method, a label-setting algorithm is also proposed. Computational tests are designed to show the effectiveness of the proposed algorithms and procedures. This dissertation also gives a literature review related to the class of scheduling problems that involve SDS times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations - single machine, parallel machine, flow shop, and job shop - reviewing both optimizing and heuristic solution methods in each category. Since lot-sizing is so intimately related to scheduling, this dissertation reviews work that integrates these issues in relationship to each configuration. This dissertation provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems. since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-time
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Jain, Himanshu. "Dynamic Simulation of Power Systems using Three Phase Integrated Transmission and Distribution System Models: Case Study Comparisons with Traditional Analysis Methods." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74234.

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Solar PV-based distributed generation has increased significantly over the last few years, and the rapid growth is expected to continue in the foreseeable future. As the penetration levels of distributed generation increase, power systems will become increasingly decentralized with bi-directional flow of electricity between the transmission and distribution networks. To manage such decentralized power systems, planners and operators need models that accurately reflect the structure of, and interactions between the transmission and distribution networks. Moreover, algorithms that can simulate the steady state and dynamics of power systems using these models are also needed. In this context, integrated transmission and distribution system modeling and simulation has become an important research area in recent years, and the primary focus so far has been on studying the steady state response of power systems using integrated transmission and distribution system models. The primary objective of this dissertation is to develop an analysis approach and a program that can simulate the dynamics of three phase, integrated transmission and distribution system models, and use the program to demonstrate the advantages of evaluating the impact of solar PV-based distributed generation on power systems dynamics using such models. To realize this objective, a new dynamic simulation analysis approach is presented, the implementation of the approach in a program is discussed, and verification studies are presented to demonstrate the accuracy of the program. A new dynamic model for small solar PV-based distributed generation is also investigated. This model can interface with unbalanced networks and change its real power output according to the incident solar irradiation. Finally, application of the dynamic simulation program for evaluating the impact of solar PV units using an integrated transmission and distribution system model is discussed. The dissertation presents a new approach for studying the impact of solar PV-based distributed generation on power systems dynamics, and demonstrates that the solar PV impact studies performed using the program and integrated transmission and distribution system models provide insights about the dynamic response of power systems that cannot be obtained using traditional dynamic simulation approaches that rely on transmission only models.
Ph. D.
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Gilbert, Frédéric. "Méthodes et modèles pour la visualisation de grandes masses de données multidimensionnelles nominatives dynamiques." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14498/document.

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La visualisation d'informations est un domaine qui connaît un réel intérêt depuis une dizaine d'années. Dernièrement, avec l'explosion des moyens de communication, l'analyse de réseaux sociaux fait l'objet de nombreux travaux de recherches. Nous présentons dans cette thèse des travaux sur l'analyse de réseaux sociaux dynamiques, c'est à dire que nous prenons en compte l'aspect temporel des données. [...]
Since ten years, informations visualization domain knows a real interest.Recently, with the growing of communications, the research on social networks analysis becomes strongly active. In this thesis, we present results on dynamic social networks analysis. That means that we take into account the temporal aspect of data. We were particularly interested in communities extraction within networks and their evolutions through time. [...]
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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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Mensah, Pernelle. "Generation and Dynamic Update of Attack Graphs in Cloud Providers Infrastructures." Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0011.

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Dans les infrastructures traditionnelles, les graphes d’attaque permettent de brosser un tableau de la sécurité, car ils sont un modèle décrivant les différentes étapes suivies par un attaquant dans le but de compromettre un actif du réseau. Ces graphes peuvent ainsi servir de base à l’évaluation automatisée des risques, en s’appuyant sur l’identification et l’évaluation des actifs essentiels. Cela permet de concevoir des contre-mesures proactives et réactives pour la réduction des risques et peut être utilisé pour la surveillance et le renforcement de la sécurité du réseau.Cette thèse vise à appliquer une approche similaire dans les environnements Cloud, ce qui implique de prendre en compte les nouveaux défis posés par ces infrastructures modernes, la majorité des graphes d’attaque étant conçue pour une application dans des environnements traditionnels. Les nouveaux scénarios d’attaque liés à la virtualisation, ainsi que les propriétés inhérentes du Cloud, à savoir l’élasticité et le caractère dynamique, sont quelques-uns des obstacles à franchir à cette fin.Ainsi, pour atteindre cet objectif, un inventaire complet des vulnérabilités liées à la virtualisation a été effectué, permettant d'inclure cette nouvelle dimension dans les graphes d'attaque existants. Par l'utilisation d'un modèle adapté à l’échelle du Cloud, nous avons pu tirer parti des technologies Cloud et SDN, dans le but de construire des graphes d’attaque et de les maintenir à jour. Des algorithmes capables de faire face aux modifications fréquentes survenant dans les environnements virtualisés ont été conçus et testés à grande échelle sur une plateforme Cloud réelle afin d'évaluer les performances et confirmer la validité des méthodes proposées dans cette thèse pour permettre à l’administrateur de Cloud de disposer d’un graphe d’attaque à jour dans cet environnent
In traditional environments, attack graphs can paint a picture of the security exposure of the environment. Indeed, they represent a model allowing to depict the many steps an attacker can take to compromise an asset. They can represent a basis for automated risk assessment, relying on an identification and valuation of critical assets in the network. This allows to design pro-active and reactive counter-measures for risk mitigation and can be leveraged for security monitoring and network hardening.Our thesis aims to apply a similar approach in Cloud environments, which implies to consider new challenges incurred by these modern infrastructures, since the majority of attack graph methods were designed with traditional environments in mind. Novel virtualization attack scenarios, as well as inherent properties of the Cloud, namely elasticity and dynamism are a cause for concern.To realize this objective, a thorough inventory of virtualization vulnerabilities was performed, for the extension of existing vulnerability templates. Based on an attack graph representation model suitable to the Cloud scale, we were able to leverage Cloud and SDN technologies, with the purpose of building Cloud attack graphs and maintain them in an up-to-date state. Algorithms able to cope with the frequent rate of change occurring in virtualized environments were designed and extensively tested on a real scale Cloud platform for performance evaluation, confirming the validity of the methods proposed in this thesis, in order to enable Cloud administrator to dispose of an up-to-date Cloud attack graph
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Siddiqui, Asher. "Capturing JUnit Behavior into Static Programs : Static Testing Framework." Thesis, Linnaeus University, School of Computer Science, Physics and Mathematics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-5510.

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In this research paper, it evaluates the benefits achievable from static testing framework by analyzing and transforming the JUnit3.8 source code and static execution of transformed code. Static structure enables us to analyze the code statically during creation and execution of test cases. The concept of research is by now well established in static analysis and testing development. The research approach is also increasingly affecting the static testing process and such research oriented work has proved particularly valuable for those of us who want to understand the reflective behavior of JUnit3.8 Framework.

JUnit3.8 Framework uses Java Reflection API to invoke core functionality (test cases creation and execution) dynamically. However, Java Reflection API allows developers to access and modify structure and behavior of a program.  Reflection provides flexible solution for creating test cases and controlling the execution of test cases. Java reflection helps to encapsulate test cases in a single object representing the test suite. It also helps to associate each test method with a test object. Where reflection is a powerful tool to perform potential operations, on the other hand, it limits static analysis. Static analysis tools often cannot work effectively with reflection.

In order to avoid the reflection, Static Testing Framework provides a static platform to analyze the JUnit3.8 source code and transform it into non-reflective version that emulates the dynamic behavior of JUnit3.8. The transformed source code has possible leverage to replace reflection with static code and does same things in an execution environment of Static Testing Framework that reflection does in JUnit3.8. More besides, the transformed code also enables execution environment of Static Testing Framework to run test methods statically. In order to measure the degree of efficiency, the implemented tool is evaluated. The evaluation of Static Testing Framework draws results for different Java projects and these statistical data is compared with JUnit3.8 results to measure the effectiveness of Static Testing Framework. As a result of evaluation, STF can be used for static creation and execution of test cases up to JUnit3.8 where test cases are not creating within a test class and where real definition of constructors is not required. These problems can be dealt as future work by introducing a middle layer to execute test fixtures for each test method and by generating test classes as per real definition of constructors.

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Books on the topic "Dynamic Graph Generation"

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Osipenko, Georgiy. Computer-oriented methods of dynamic systems. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1912470.

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The methods of studying the global properties of dynamic systems based on the construction of a symbolic image of this system are considered. A symbolic image is an oriented graph, which is an approximation to a dynamical system and is constructed by discretizing the phase space. The symbolic dynamics generated by the oriented graph reflects the dynamics of the system under study. The symbolic image is a tool of theoretical research and the basis of computer-oriented methods for the numerical study of nonlocal properties of dynamical systems. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the field of Applied Mathematics and Computer Science. It will be useful for graduate students and researchers studying dynamical systems and their applications.
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Mikov, Aleksandr. Generalized graphs and grammars. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1013698.

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The textbook deals with ordinary graphs and their generalizations-hypergraphs, hierarchical structures, geometric graphs, random and dynamic graphs. Graph grammars are considered in detail. Meets the requirements of the federal state educational standards of higher education of the latest generation. For master's students studying in the areas of the 02.00.00 group "Computer and Information Sciences", and can also be used in senior bachelor's courses and other areas in the field of computer science and computer engineering.
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Reynolds, Alan. Income and Wealth. Greenwood, 2006. http://dx.doi.org/10.5040/9798400669460.

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Why some people are rich and others poor can be explained in a number of ways.Income and Wealthfocuses on who gets what and why. It explains the dynamics of income generation, how it is measured, and how such dramatic disparities in distribution come about. The book first defines various characteristics of income, with an emphasis on the gap between the rich and the poor, and reviews several theories to explain the disparities. Subsequent chapters discuss such timely topics as the vanishing middle class and the sky-high salaries of CEOs, Hollywood stars, and athletes. The final chapters consider the implications of policies, such as the minimum wage, taxes, immigration, and trade quotas, and expand the discussion to consider international comparisons. Featuring graphs and charts, a glossary of key terms, and a listing of references and resources,Income and Wealthexplains the intricate, and often controversial, effects of economic policies on individuals, families, and communities. Moreover, it shows how the numbers can be manipulated by policymakers, pundits, journalists, and academics to promote various agendas—and shows readers how to recognize hyberbole and make better-informed decisions.
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Book chapters on the topic "Dynamic Graph Generation"

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Grammatikakis, Konstantinos-Panagiotis, and Nicholas Kolokotronis. "Attack Graph Generation." In Cyber-Security Threats, Actors, and Dynamic Mitigation, 281–334. Boca Raton: CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003006145-8.

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Zhang, Wenbin, Liming Zhang, Dieter Pfoser, and Liang Zhao. "Disentangled Dynamic Graph Deep Generation." In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 738–46. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976700.83.

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Khademi, Mahmoud, and Oliver Schulte. "Dynamic Gated Graph Neural Networks for Scene Graph Generation." In Computer Vision – ACCV 2018, 669–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20876-9_42.

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Fan, Feifan, Runwei Qiang, Chao Lv, Wayne Xin Zhao, and Jianwu Yang. "Tweet Timeline Generation via Graph-Based Dynamic Greedy Clustering." In Information Retrieval Technology, 304–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28940-3_24.

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Hu, Lingfeng, Si Liu, and Hanzi Wang. "An Effective Dynamic Reweighting Method for Unbiased Scene Graph Generation." In Pattern Recognition and Computer Vision, 345–56. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8429-9_28.

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Krause, Franz, Kabul Kurniawan, Elmar Kiesling, Jorge Martinez-Gil, Thomas Hoch, Mario Pichler, Bernhard Heinzl, and Bernhard Moser. "Leveraging Semantic Representations via Knowledge Graph Embeddings." In Artificial Intelligence in Manufacturing, 71–85. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_5.

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AbstractThe representation and exploitation of semantics has been gaining popularity in recent research, as exemplified by the uptake of large language models in the field of Natural Language Processing (NLP) and knowledge graphs (KGs) in the Semantic Web. Although KGs are already employed in manufacturing to integrate and standardize domain knowledge, the generation and application of corresponding KG embeddings as lean feature representations of graph elements have yet to be extensively explored in this domain. Existing KGs in manufacturing often focus on top-level domain knowledge and thus ignore domain dynamics, or they lack interconnectedness, i.e., nodes primarily represent non-contextual data values with single adjacent edges, such as sensor measurements. Consequently, context-dependent KG embedding algorithms are either restricted to non-dynamic use cases or cannot be applied at all due to the given KG characteristics. Therefore, this work provides an overview of state-of-the-art KG embedding methods and their functionalities, identifying the lack of dynamic embedding formalisms and application scenarios as the key obstacles that hinder their implementation in manufacturing. Accordingly, we introduce an approach for dynamizing existing KG embeddings based on local embedding reconstructions. Furthermore, we address the utilization of KG embeddings in the Horizon2020 project Teaming.AI (www.teamingai-project.eu.) focusing on their respective benefits.
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Yu, Hong Qing. "Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System." In Advances in Intelligent Systems and Computing, 30–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63092-8_3.

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Xiong, Yun, Yao Zhang, Hanjie Fu, Wei Wang, Yangyong Zhu, and Philip S. Yu. "DynGraphGAN: Dynamic Graph Embedding via Generative Adversarial Networks." In Database Systems for Advanced Applications, 536–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18576-3_32.

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Cao, Yuan, Rafael Fuchs, and Anita Keshmirian. "Enhancing Argument Generation Using Bayesian Networks." In Robust Argumentation Machines, 253–65. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63536-6_15.

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AbstractIn this paper, we examine algorithms that utilize factor graphs from Bayesian Belief Networks to generate and evaluate arguments. We assess their strengths and weaknesses, which leads to the creation of our improved algorithm that rectifies the issues that we identified. Our approach includes applying the original and modified algorithms to previously known networks to pose challenges in generating robust arguments for humans and computers. Our findings reveal significant improvements in the creation of more robust arguments. Moreover, we delve into the dynamics of argument interaction, offering detailed insight into the algorithms’ practical efficacy.
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Allen, Robert B. "Using Causal Threads to Explain Changes in a Dynamic System." In Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration, 211–19. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8088-8_18.

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AbstractWe explore developing rich semantic models of systems. Specifically we consider structured causal explanations about state changes in those systems. Essentially, we are developing process-based dynamic knowledge graphs. As an example, we construct a model of the causal threads for geological changes proposed by the Snowball Earth theory. Further, we describe an early prototype of a graphical interface to present the explanations. Unlike statistical approaches to summarization and explanation such as Large Language Models (LLMs), our approach of direct representation can be inspected and verified directly.
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Conference papers on the topic "Dynamic Graph Generation"

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You, Sisi, and Bing-Kun Bao. "Dynamic Scene Graph Generation with Unified Temporal Modeling." In 2024 IEEE International Conference on Multimedia and Expo (ICME), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687612.

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Gao, Dequan, Jiwei Li, Xuewei Ding, Bao Feng, Zhifan Wang, and Linfeng Zhang. "Database Alarm Reasoning with Event Knowledge Graph Based on Graph Attention Network and Dynamic Pattern Matching." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN), 255–62. IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744104.

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Wang, Song, Zhenming Zhang, Wei Li, Chen Yin, Yu Ma, and Weiyao Xu. "Dynamic Residual Graph Attention Network for Network Intrusion Detection System." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN), 53–56. IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744080.

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Khandelwal, Anant. "FloCoDe: Unbiased Dynamic Scene Graph Generation with Temporal Consistency and Correlation Debiasing." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2516–26. IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00258.

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Chang, Che, Cheng-Hsiang Chiu, Boyang Zhang, and Tsung-Wei Huang. "Incremental Critical Path Generation for Dynamic Graphs." In 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 771–74. IEEE, 2024. http://dx.doi.org/10.1109/isvlsi61997.2024.00150.

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Liang, Xun, Hanyu Wang, Shichao Song, Mengting Hu, Xunzhi Wang, Zhiyu Li, Feiyu Xiong, and Bo Tang. "Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs." In Findings of the Association for Computational Linguistics ACL 2024, 5797–814. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.345.

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Yu, Zidong, Changhe Zhang, Xiaoyun Wang, and Chao Deng. "End-to-End Hand Gesture Recognition Based on Dynamic Graph Topology Generating Mechanism and Weighted Graph Isomorphism Network." In 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/m2vip62491.2024.10746060.

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Kim, Daesik, YoungJoon Yoo, Jeesoo Kim, Sangkuk Lee, and Nojun Kwak. "Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00438.

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Zhou, Hao, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. "Commonsense Knowledge Aware Conversation Generation with Graph Attention." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/643.

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Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understanding of the post. Then, during word generation, the model attentively reads the retrieved knowledge graphs and the knowledge triples within each graph to facilitate better generation through a dynamic graph attention mechanism. This is the first attempt that uses large-scale commonsense knowledge in conversation generation. Furthermore, unlike existing models that use knowledge triples (entities) separately and independently, our model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Experiments show that the proposed model can generate more appropriate and informative responses than state-of-the-art baselines.
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Liang, Jiafeng, Yuxin Wang, Zekun Wang, Ming Liu, Ruiji Fu, Zhongyuan Wang, and Bing Qin. "GTR: A Grafting-Then-Reassembling Framework for Dynamic Scene Graph Generation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/131.

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Dynamic scene graph generation aims to identify visual relationships (subject-predicate-object) in frames based on spatio-temporal contextual information in the video. Previous work implicitly models the spatio-temporal interaction simultaneously, which leads to entanglement of spatio-temporal contextual information. To this end, we propose a Grafting-Then-Reassembling framework (GTR), which explicitly extracts intra-frame spatial information and inter-frame temporal information in two separate stages to decouple spatio-temporal contextual information. Specifically, we first graft a static scene graph generation model to generate static visual relationships within frames. Then we propose the temporal dependency model to extract the temporal dependencies across frames, and explicitly reassemble static visual relationships into dynamic scene graphs. Experimental results show that GTR achieves the state-of-the-art performance on Action Genome dataset. Further analyses reveal that the reassembling stage is crucial to the success of our framework.
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