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TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature. (arXiv:2205.12784v1 [cs.LG])
May 26, 2022, 1:10 a.m. | Cuiying Huo, Di Jin, Chundong Liang, Dongxiao He, Tie Qiu, Lingfei Wu
cs.LG updates on arXiv.org arxiv.org
Trust evaluation is critical for many applications such as cyber security,
social communication and recommender systems. Users and trust relationships
among them can be seen as a graph. Graph neural networks (GNNs) show their
powerful ability for analyzing graph-structural data. Very recently, existing
work attempted to introduce the attributes and asymmetry of edges into GNNs for
trust evaluation, while failed to capture some essential properties (e.g., the
propagative and composable nature) of trust graphs. In this work, we propose a …
arxiv evaluation graph graph neural network network neural network trust
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