2 citations · 4 across the 7 of their papers we have counts for
8 papers
Adversarial Bias: Data Poisoning Attacks on Fairness
Eunice Chan, Hanghang Tong
With the growing adoption of AI and machine learning systems in real-world applications, ensuring their fairness has become increasingly critical. The majority of the work in algor…
Hephaestus: Mixture Generative Modeling with Energy Guidance for Large-scale QoS Degradation
Nguyen Do, Bach Ngo, Youval Kashuv +3
We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastr…
Time Will Tell: Harness Time to Denoise Collaborative Filtering
Xinyu He, Jose Sepulveda, Mostafa Rahmani +3
Neural graph collaborative filtering is a powerful tool for recommendation due to its superb capability of capturing the user-item interaction. Despite the substantial progress, th…
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method
Weilin Cong, Jian Kang, Hanghang Tong +1
Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves ov…
Conversational Question Answering with Reformulations over Knowledge Graph
Lihui Liu, Blaine Hill, Boxin Du +2
Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-ar…
Certified Defense on the Fairness of Graph Neural Networks
Yushun Dong, Binchi Zhang, Hanghang Tong +1
Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years. Nevertheless, due to the vulnerabilities of GNNs, it has…
Deceptive Fairness Attacks on Graphs via Meta Learning
Jian Kang, Yinglong Xia, Ross Maciejewski +2
We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively? W…
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…