2 citations · 4 across the 4 of their papers we have counts for
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cs.LG2023
Hierarchical Multi-Marginal Optimal Transport for Network Alignment
Zhichen Zeng, Boxin Du, Si Zhang +3
Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning…
cs.LG2023
Neural Multi-network Diffusion towards Social Recommendation
Boxin Du, Lihui Liu, Jiejun Xu +2
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommend…
cs.LG2021★ 2 cited
Hypergraph Pre-training with Graph Neural Networks
Boxin Du, Changhe Yuan, Robert Barton +2
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus…