4 citations · 9 across the 5 of their papers we have counts for
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cs.LG2022★ 4 cited
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
Cuiying Huo, Di Jin, Yawen Li +3
Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on comp…
cs.LG2022
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
Cuiying Huo, Di Jin, Chundong Liang +3
Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a g…
cs.LG2022★ 3 cited
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
Cuiying Huo, Dongxiao He, Yawen Li +5
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or s…