Abstract
Graph Convolutional Networks (GCNs) have been proven to be effective in various graph-related tasks, such as community detection. Essentially graph convolution is simply a special form of Laplacian smoothing, acting as a low-pass filter that makes the features of nodes linked to each other similarly. For community detection, however, the similarity of intra-community nodes and the difference of inter-community nodes are equally vital. To bridge the gap between GCNs and community detection, we develop a novel Community-Centric Dual Filter (CCDF) framework for community detection. The central idea is that, besides of low-pass filter in GCN, we define network modularity enhanced high-pass filter to separate the discriminative signals from the raw features. In addition, we design a scheme to jointly optimize low-frequency and high-frequency information extraction on statistical modeling of Markov Random Fields. Extensive experiments demonstrate that the proposed CCDF model can consistently outperform or match state-of-the-art baselines in terms of semi-supervised community detection.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61773215, 61802206).
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Ai, G., Yan, H., Yang, J., Li, X. (2021). Beyond Laplacian Smoothing for Semi-supervised Community Detection. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_15
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