358 citations · 669 across the 35 of their papers we have counts for
35 papers
Disambiguated Node Classification with Graph Neural Networks
Tianxiang Zhao, Xiang Zhang, Suhang Wang
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains. Despite their great successful, one critical chall…
Active Learning for Graphs with Noisy Structures
Hongliang Chi, Cong Qi, Suhang Wang +1
Graph Neural Networks (GNNs) have seen significant success in tasks such as node classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the exce…
Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
Fali Wang, Tianxiang Zhao, Suhang Wang
Few-shot node classification poses a significant challenge for Graph Neural Networks (GNNs) due to insufficient supervision and potential distribution shifts between labeled and un…
Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback
Teng Xiao, Suhang Wang
Probabilistic learning to rank (LTR) has been the dominating approach for optimizing the ranking metric, but cannot maximize long-term rewards. Reinforcement learning models have b…
Shape-aware Graph Spectral Learning
Junjie Xu, Enyan Dai, Dongsheng Luo +2
Spectral Graph Neural Networks (GNNs) are gaining attention for their ability to surpass the limitations of message-passing GNNs. They rely on supervision from downstream tasks to…
Learning How to Propagate Messages in Graph Neural Networks
Teng Xiao, Zhengyu Chen, Donglin Wang +1
This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the…
Towards Fair Graph Neural Networks via Graph Counterfactual
Zhimeng Guo, Jialiang Li, Teng Xiao +2
Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent w…
STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation
Wanda Li, Wenhao Zheng, Xuanji Xiao +1
Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users. As directly optimizing user retention is challeng…
Fairness-aware Message Passing for Graph Neural Networks
Huaisheng Zhu, Guoji Fu, Zhimeng Guo +3
Graph Neural Networks (GNNs) have shown great power in various domains. However, their predictions may inherit societal biases on sensitive attributes, limiting their adoption in r…
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang +5
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can foo…
Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
Tianxiang Zhao, Wenchao Yu, Suhang Wang +6
Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations. However, exi…
Self-Explainable Graph Neural Networks for Link Prediction
Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critic…
Counterfactual Learning on Graphs: A Survey
Zhimeng Guo, Teng Xiao, Zongyu Wu +3
Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success i…
Unnoticeable Backdoor Attacks on Graph Neural Networks
Enyan Dai, Minhua Lin, Xiang Zhang +1
Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to…
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critic…
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Graph serves as a powerful tool for modeling data that has an underlying structure in non-Euclidean space, by encoding relations as edges and entities as nodes. Despite development…
HP-GMN: Graph Memory Networks for Heterophilous Graphs
Junjie Xu, Enyan Dai, Xiang Zhang +1
Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption…
Towards Prototype-Based Self-Explainable Graph Neural Network
Enyan Dai, Suhang Wang
Graph Neural Networks (GNNs) have shown great ability in modeling graph-structured data for various domains. However, GNNs are known as black-box models that lack interpretability.…
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Shijie Zhou, Zhimeng Guo, Charu Aggarwal +2
Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homo…
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
Tianxiang Zhao, Xiang Zhang, Suhang Wang
In recent years, graph neural networks (GNNs) have achieved state-of-the-art performance for node classification. However, most existing GNNs would suffer from the graph imbalance…
Decoupled Self-supervised Learning for Non-Homophilous Graphs
Teng Xiao, Zhengyu Chen, Zhimeng Guo +2
This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is…
Towards Faithful and Consistent Explanations for Graph Neural Networks
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critic…
Learning Fair Models without Sensitive Attributes: A Generative Approach
Huaisheng Zhu, Enyan Dai, Hui Liu +1
Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues.…
Exploring Edge Disentanglement for Node Classification
Tianxiang Zhao, Xiang Zhang, Suhang Wang
Edges in real-world graphs are typically formed by a variety of factors and carry diverse relation semantics. For example, connections in a social network could indicate friendship…