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    Chapter and Conference Paper

    Learning Robust Representation Through Graph Adversarial Contrastive Learning

    Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features. Thus, i...

    Jiayan Guo, Shangyang Li, Yue Zhao, Yan Zhang in Database Systems for Advanced Applications (2022)

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    Chapter and Conference Paper

    An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning

    Mining graph data has gained wide attention in modern applications. With the explosive growth of graph data, it is common to see many of them collected and stored in different distinction systems. These local ...

    Jiayan Guo, Shangyang Li, Yan Zhang in Database Systems for Advanced Applications (2023)

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    Chapter and Conference Paper

    Can Chatbot Anthropomorphism and Empathy Mitigate the Impact of Customer Anger on Satisfaction?

    When customers initiate inquiries with negative emotions following a service failure, whether chatbot service agents can alleviate the undesirable outcomes resulting from negative emotions poses significant ch...

    Jian Tang, Yunran Wang, **nxue Zhou, Jiayan Guo in Wisdom, Well-Being, Win-Win (2024)