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.

In this paper, we introduce a novel method to discover common and distinct structural connectivity patterns between SZP and MDD via a Cluster-Driven Nonnegative Matrix Factorization (called CD-NMF). Specifically, CD-NMF is applied to decompose the joint structural connectivity map into common and distinct parts, and each part is further factorized into two sub-matrices (i.e. common/distinct basis matrix and common/distinct encoding matrix) correspondingly. By imposing the clustering constraints on common and distinct encoding matrices, the discriminative patterns as well as the common patterns between the two disorders are extracted simultaneously. Experimental results demonstrate that CD-NMF allows finding the common and distinct structural patterns effectively. More importantly, the derived distinct patterns, show powerful ability to discriminate the patients of schizophrenia and major depressive disorder.