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Found 5 papers, 2 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.
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations.
Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction
To alleviate this issue, we propose a novel DDI prediction method based on relation-aware graph structure embedding with co-contrastive learning, RaGSECo.
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.
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