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20152026
most citedPredicting the evolution of complex networks via local information

13 citations · 24 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.SI2024

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…

cs.SI2022★ 5 cited

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…

cs.SI2018★ 1 cited

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…

cs.SI2016★ 13 cited

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…

cs.SI2015★ 2 cited

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…