24 citations · 110 across the 14 of their papers we have counts for
4 papers · 1 filter
Unifying Homophily and Heterophily Network Transformation via Motifs
Yan Ge, Jun Ma, Li Zhang +1
Higher-order proximity (HOP) is fundamental for most network embedding methods due to its significant effects on the quality of node embedding and performance on downstream network…
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,…
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…
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…