91 citations · 102 across the 6 of their papers we have counts for
6 papers
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
Mengying Jiang, Guizhong Liu, Yuanchao Su +1
In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies, leading to performance degradation. Furtherm…
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
Mengying Jiang, Guizhong Liu, Yuanchao Su +2
Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit c…
Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction
Mengying Jiang, Guizhong Liu, Biao Zhao +2
Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-…
Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning
Weiqiang Jin, Biao Zhao, Hang Yu +3
Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer. As a com…
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily
Mengying Jiang, Guizhong Liu, Yuanchao Su +1
In representation learning on the graph-structured data, under heterophily (or low homophily), many popular GNNs may fail to capture long-range dependencies, which leads to their p…
R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
Xinliang Wu, Mengying Jiang, Guizhong Liu
Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular…