Static-Graph Estimation in Hybrid GNNs Using Behavioral-Similarity and Clustering-Based Methods

Hybrid GNNs, which learn both long-term structural information encoded in static graphs and temporal interactions within dynamic graphs, have attracted attention for their high predictive accuracy. In practical applications, however, the static relational data required as part of the input is not al...

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Bibliografiske detaljer

Principais autores:	Ryusei Otani, Keiichi Namikoshi, Yuko Sakurai, Mingyu Guo, Satoshi Oyama
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2026-01-01
Serier:	IEEE Access
Fag:	Graph neural networks hybrid GNN static graph behavioral similarity
Online adgang:	https://ieeexplore.ieee.org/document/11435972/
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