Mar 15, 2023 · In this paper, we propose a query-driven Temporal Graph Convolutional Network (CS-TGN) that can capture flexible community structures by learning from the ...
Mar 15, 2023 · In this paper, we propose a query-driven Temporal Graph Convolutional Network. (CS-TGN) that can capture flexible community structures by learn-.
CS-TGN first combines the local query-dependent structure and the global graph embedding in each snapshot of the network and then uses a GRU cell with ...
Network Statistics · Interactive community search on brain networks · CS-TGN: Community Search via Temporal Graph Neural Networks. Preprint. Full-text available.
Jul 17, 2024 · CS-TGN: Community Search via Temporal Graph Neural Networks. CoRR abs/2303.08964 (2023). [i1]. view. electronic edition via DOI (open access) ...
Many algorithms are proposed in studying subgraph problems, where one common approach is by extracting the patterns and structures of a given graph. 1.
In this paper, we propose Graph Neural Network (GNN) models for both CS and ACS problems, i.e., Query Driven-GNN (QD-GNN) and Attributed Query Driven-GNN (AQD- ...
CS-TGN: Community Search via Temporal Graph Neural Networks ... The evolution of these networks over time has motivated several recent studies to identify local ...
공동 저자 ; Cs-tgn: Community search via temporal graph neural networks. F Hashemi, A Behrouz, M Rezaei Hajidehi. Companion Proceedings of the ACM Web Conference ...
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In this paper, we present Temporal Graph Networks (TGNs), a generic, efficient framework for deep learning on dynamic graphs represented as sequences of timed ...
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