8 citations · 12 across the 18 of their papers we have counts for
10 papers · 1 filter
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…
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
Xing Ai, Guanyu Zhu, Yulin Zhu +4
Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding gene…
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…
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
Xing Ai, Jialong Zhou, Yulin Zhu +4
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. Howev…
Robust Graph Contrastive Learning with Information Restoration
Yulin Zhu, Xing Ai, Yevgeniy Vorobeychik +1
The graph contrastive learning (GCL) framework has gained remarkable achievements in graph representation learning. However, similar to graph neural networks (GNNs), GCL models are…
Simple yet Effective Gradient-Free Graph Convolutional Networks
Yulin Zhu, Xing Ai, Qimai Li +2
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. Compared with nonlinear Graph Neural Network (GNN) models,…
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Yulin Zhu, Liang Tong, Gaolei Li +2
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models. We present FocusedCleaner as a poisoned…
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…