This paper presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. Motivated by this observation, the proposed MGCN is designed to learn expressive node representations by considering the following two aspects. First, we propose a novel Laplacian operator to generate weights for feature aggregation that can adaptively capture the feature similarity among nodes. Second, based on modularity optimization, we design a novel auxiliary loss that enables the weights for aggregating features to capture the cluster structure of the graph. The proposed MGCN, therefore, can learn expressive representations for each node in the graph by leveraging the modularized messages, i.e., predominately aggregating the features of neighbors in the same cluster. The proposed MGCN has been tested with several well-established datasets and compared with strong GCN-based models. The experimental results demonstrate that MGCN is more effective in various downstream tasks.
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There was no specific funding for the research done