8 citations · 11 across the 17 of their papers we have counts for
8 papers · 1 filter
Provably Robust Adaptation for Language-Empowered Foundation Models
Yuni Lai, Xiaoyu Xue, Linghui Shen +5
Language-empowered foundation models (LeFMs), such as CLIP and GraphCLIP, have transformed multimodal learning by aligning visual (or graph) features with textual representations,…
Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting
Zhenan Lin, Yuni Lai, Wai Lun Lo +5
Time-evolving traffic flow forecasting are playing a vital role in intelligent transportation systems and smart cities. However, the dynamic traffic flow forecasting is a highly no…
AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing
Yuni Lai, Yulin Zhu, Yixuan Sun +6
Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness via randomized smoothing offers provable guarant…
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models
Xiao Yang, Kai Zhou, Yuni Lai +1
With the trend of large graph learning models, business owners tend to employ a model provided by a third party to deliver business services to users. However, these models might b…
Adversarial Robustness of Link Sign Prediction in Signed Graphs
Jialong Zhou, Xing Ai, Yuni Lai +7
Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as th…
Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similarity
Yulin Zhu, Yuni Lai, Xing Ai +7
Despite the tremendous success of graph-based learning systems in handling structural data, it has been widely investigated that they are fragile to adversarial attacks on homophil…
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks
Yuni Lai, Yulin Zhu, Bailin Pan +1
Deep Graph Learning (DGL) has emerged as a crucial technique across various domains. However, recent studies have exposed vulnerabilities in DGL models, such as susceptibility to e…
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
Yulin Zhu, Yuni Lai, Kaifa Zhao +4
Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques. These GAD to…