Results 1 to 10 of about 8,004,238 (365)
Persistent homology in graph power filtrations. [PDF]
R Soc Open Sci, 2016 The persistence of homological features in simplicial complex representations of big datasets in Rn resulting from Vietoris–Rips or Čech filtrations is commonly used to probe the topological structure of such datasets.
Parks AD, Marchette DJ.
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A Review of Graph Neural Networks and Their Applications in Power Systems [PDF]
Journal of Modern Power Systems and Clean Energy, 2022 Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains.
Wenlong Liao +4 more
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Recent developments on the power graph of finite groups – a survey
AKCE International Journal of Graphs and Combinatorics, 2021 Algebraic graph theory is the study of the interplay between algebraic structures (both abstract as well as linear structures) and graph theory. Many concepts of abstract algebra have facilitated through the construction of graphs which are used as tools
Ajay Kumar +3 more
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Power domination in maximal planar graphs [PDF]
Discrete Mathematics & Theoretical Computer Science, 2019 Power domination in graphs emerged from the problem of monitoring an electrical system by placing as few measurement devices in the system as possible. It corresponds to a variant of domination that includes the possibility of propagation.
Paul Dorbec +2 more
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Conceptual design of a decision knowledge service model integrating a multi-agent supply relationship diagram for electric power emergency equipment. [PDF]
Front Big DataIntroductionThe decision regarding the supply of emergency equipments for power emergencies requires timeliness, efficiency, and accuracy. The multi-agent supply relationship graph, based on complex data fusion, enables the comprehensive exploration of ...
Si J +7 more
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Power System Network Topology Identification Based on Knowledge Graph and Graph Neural Network
Frontiers in Energy Research, 2021 The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their ...
Changgang Wang, Jun An, Jun An, Gang Mu
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Rethinking the Expressive Power of GNNs via Graph Biconnectivity [PDF]
International Conference on Learning Representations, 2023 Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data. While numerous approaches have been proposed to improve GNNs in terms of the Weisfeiler-Lehman (WL) test, generally there is still a lack of deep ...
Bohang Zhang +3 more
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On the Expressive Power of Geometric Graph Neural Networks [PDF]
International Conference on Machine Learning, 2023 The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and the WL framework are inapplicable for geometric graphs embedded in Euclidean space ...
Chaitanya K. Joshi, Simon V. Mathis
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The expressive power of pooling in Graph Neural Networks [PDF]
Neural Information Processing Systems, 2023 In Graph Neural Networks (GNNs), hierarchical pooling operators generate local summaries of the data by coarsening the graph structure and the vertex features.
F. Bianchi, Veronica Lachi
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The Expressive Power of Graph Neural Networks: A Survey [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power.
Bingxue Zhang +6 more
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