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Yu, Zhongjing; Wang, Han; Liu, Yang; Bohm, Christian; Shao, Junming (2020): Community Attention Network for Semi-supervised Node Classification. In: 20th IEEE International Conference on Data Mining (ICDM 2020): pp. 1382-1387
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Graph neural networks (GNNs) have achieved great success for semi-supervised node classification by embedding node representation into a low-dimensional space.. However, existing approaches usually ignore one intrinsic property of graphs: community structure, where the formation of distinct communities in graphs is often driven by different subset of attributes. In this paper, we introduce a new method, called Community Attention Network (CAT), aiming to extract community-specific features and then enhance node embeddings for classification. To learn such community-specific information, we design a new loss function to ensure the nodes in the same community should share similar attributes (i.e., low covariance), and any unlabelled node should belong to only one class with high probability i.e.,( low community distribution entropy) in a community attention network. Extensive experimental results demonstrate the effectiveness of CAT and its advantages over many state-of-the-art approaches. To further illustrate the benefits of CAT to capture the community information, a case study is given and discussed.