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pro vyhledávání: '"Xian, Xingping"'
Graph Neural Networks (GNNs) have garnered significant attention and have been extensively utilized across various domains. However, similar to other deep learning models, GNNs are often viewed as black-box models, making it challenging to interpret
Externí odkaz:
http://arxiv.org/abs/2409.15698
Autor:
Wu, Tao, Cao, Xinwen, Wang, Chao, Qiao, Shaojie, Xian, Xingping, Yuan, Lin, Cui, Canyixing, Liu, Yanbing
Graph Neural Networks (GNNs) have demonstrated significant application potential in various fields. However, GNNs are still vulnerable to adversarial attacks. Numerous adversarial defense methods on GNNs are proposed to address the problem of adversa
Externí odkaz:
http://arxiv.org/abs/2406.13499
Graph neural networks (GNNs) have achieved tremendous success, but recent studies have shown that GNNs are vulnerable to adversarial attacks, which significantly hinders their use in safety-critical scenarios. Therefore, the design of robust GNNs has
Externí odkaz:
http://arxiv.org/abs/2406.13920
Autor:
Xian, Xingping, Wu, Tao, Ma, Xiaoke, Qiao, Shaojie, Shao, Yabin, Wang, Chao, Yuan, Lin, Wu, Yu
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 in deep learning, graph neural networks have been used for link predi
Externí odkaz:
http://arxiv.org/abs/2301.00169
Publikováno v:
In Data & Knowledge Engineering January 2024 149
Akademický článek
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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 be a fundamental challenge. Exploring the regular information and iden
Externí odkaz:
http://arxiv.org/abs/1805.07746
Autor:
Wu, Tao, Yang, Nan, Chen, Long, Xiao, Xiaokui, Xian, Xingping, Liu, Jun, Qiao, Shaojie, Cui, Canyixing
Publikováno v:
In Information Sciences December 2022 617:234-253
Publikováno v:
In Information Sciences March 2022 587:794-812
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 cascades evolve in time? When and which users will adopt a certain message?
Externí odkaz:
http://arxiv.org/abs/1512.08455