Graph Convolutional Neural Networks With Diverse Negative Samples via Decomposed Determinant Point Processes

Graph convolutional neural networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a message-passing mechanism, each node aggregates information from its first-order neighbor to update i...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-12, Vol.35 (12), p.18160-18171
Hauptverfasser:	Duan, Wei, Xuan, Junyu, Qiao, Maoying, Lu, Jie
Format:	Artikel
Sprache:	eng
Schlagworte:	Aggregates Computational efficiency Convolutional neural networks Determinant point processes diversity Eigenvalues and eigenfunctions Graph neural networks graph neural networks (GNNs) negative sampling Topology
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