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Found 5 papers, 3 papers with code
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
The proposed GCN-SA contains two enhancements corresponding to edges and node features.
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
GCN-SL: Graph Convolutional Network with Structure Learning for Disassortative Graphs
To solve the above-mentioned issue, we propose a graph convolutional network with structure learning (GCN-SL), and furthermore, the proposed approach can be applied to node classification.
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily
The proposed GCN-SL can aggregate feature representations from nearby nodes via re-connected adjacency matrix and is applied to graphs with various levels of homophily.
R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
Heterogeneous graph is a kind of data structure widely existing in real life.
