213 citations · 238 across the 5 of their papers we have counts for
5 papers
DyExplainer: Explainable Dynamic Graph Neural Networks
Tianchun Wang, Dongsheng Luo, Wei Cheng +2
Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box…
Time Series Contrastive Learning with Information-Aware Augmentations
Dongsheng Luo, Wei Cheng, Yingheng Wang +8
Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been l…
Attentive Social Recommendation: Towards User And Item Diversities
Dongsheng Luo, Yuchen Bian, Xiang Zhang +1
Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings. However, user/item diversities…
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu +4
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along edges of the given graph.…
Parameterized Explainer for Graph Neural Network
Dongsheng Luo, Wei Cheng, Dongkuan Xu +4
Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the loca…