13 citations · 24 across the 9 of their papers we have counts for
9 papers
GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
Canyixing Cui, Tao Wu, Xingping Xian +3
Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and asso…
Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks
Mao Wang, Tao Wu, Xingping Xian +3
Graphs effectively characterize relational data, driving graph representation learning methods that uncover underlying predictive information. As state-of-the-art approaches, Graph…
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
Xingping Xian, Jianlu Liu, Chao Wang +4
Explainability is crucial for the application of black-box Graph Neural Networks (GNNs) in critical fields such as healthcare, finance, cybersecurity, and more. Various feature att…
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
Tao Wu, Xinwen Cao, Chao Wang +5
Graph Neural Networks (GNNs) have demonstrated significant application potential in various fields. However, GNNs are still vulnerable to adversarial attacks. Numerous adversarial…
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
Tao Wu, Canyixing Cui, Xingping Xian +4
Recent studies have shown that graph neural networks (GNNs) are vulnerable to adversarial attacks, posing significant challenges to their deployment in safety-critical scenarios. T…
Generative Graph Neural Networks for Link Prediction
Xingping Xian, Tao Wu, Xiaoke Ma +5
Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances…
Network Reconstruction and Controlling Based on Structural Regularity Analysis
Tao Wu, Shaojie Qiao, Xingping Xian +3
From the perspective of network analysis, the ubiquitous networks are comprised of regular and irregular components, which makes uncovering the complexity of network structures to…
Predicting the evolution of complex networks via local information
Tao Wu, Leiting Chen
Almost all real-world networks are subject to constant evolution, and plenty of evolving networks have been investigated to uncover the underlying mechanisms for a deeper understan…
Full-scale Cascade Dynamics Prediction with a Local-First Approach
Tao Wu, Leiting Chen, Xingping Xian +1
Information cascades are ubiquitous in various social networking web sites. What mechanisms drive information diffuse in the networks? How does the structure and size of the cascad…