24 citations · 74 across the 10 of their papers we have counts for
Showing 2020 · cs.LGShow all
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cs.LG2020★ 4 cited
Hop-Hop Relation-aware Graph Neural Networks
Li Zhang, Yan Ge, Haiping Lu
Graph Neural Networks (GNNs) are widely used in graph representation learning. However, most GNN methods are designed for either homogeneous or heterogeneous graphs. In this paper,…
cs.LG2020
GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
Hao Xu, Shengqi Sang, Peizhen Bai +2
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks. Existing…
cs.LG2020★ 11 cited
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
Hao Xu, Shengqi Sang, Haiping Lu
The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and protei…