Affiliations: [a] School of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China | [b] Mathematics and Computer Science, Physics and Natural Science Department, University of Messina, V.le F. Stagno D’Alcontres, Messina, Italy | [c] Department of Computer Science, University of Messina, V.le F. Stagno D’Alcontres, Messina, Italy
Correspondence:
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Corresponding author: Xiaoyang Liu, School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China. E-mail: [email protected].
Abstract: Traditional community detection models either ignore the feature space information and require a large amount of domain knowledge to define the meta-paths manually, or fail to distinguish the importance of different meta-paths. To overcome these limitations, we propose a novel heterogeneous graph community detection method (called KGNN_HCD, heterogeneous graph Community Detection method based on K-nearest neighbor Graph Neural Network). Firstly, the similarity matrix is generated to construct the topological structure of K-nearest neighbor graph; secondly, the meta-path information matrix is generated using a meta-path transformation layer (Mp-Trans Layer) by adding weighted convolution; finally, a graph convolutional network (GCN) is used to learn high-quality node representation, and the k-means algorithm is adopted on node embeddings to detect the community structure. We perform extensive experiments and on three heterogeneous datasets, ACM, DBLP and IMDB, and we consider as competitors 11 community detection methods such as CP-GNN and GTN. The experimental results show that the proposed KGNN_HCD method improves 2.54% and 2.56% on the ACM dataset, 2.59% and 1.47% on the DBLP dataset, and 1.22% and 1.67% on the IMDB dataset for both NMI and ARI. Experiments findings suggest that the proposed KGNN_HCD method is reasonable and effective, and KGNN_HCD can be applied to complex network classification and clustering tasks.
