4 citations · 10 across the 6 of their papers we have counts for
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cs.SI2020★ 1 cited
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…
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,…