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[34].ZHANG Zhen,ZHANG Xinfang, GAO Siihan
[35].（School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001,China；
[36].School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001,China；.）
[37].Abstract: Graph representation learning has attracted extensive attention in the field of community detection. However, existing methods neglect the effective fusion of heterogeneity and network feature structure information, are highly dependent on the distinguishability of features, and fail to fully combine with the community detection task. Therefore, this paper proposes a heterogeneous network community detection model based on a graph masked autoencoder and an attention mechanism. Firstly, the mask preprocessing module is optimized: nodes are pre-clustered, followed by dynamic masking, and noise is introduced to enhance the robustness of the graph masked autoencoder and the performance of feature reconstruction. Secondly, a heterogeneous network hierarchical encoder integrating spatial attention is designed to encode the node features of the heterogeneous network and the structure information based on meta-paths. Finally, self-training clustering loss, feature reconstruction loss, and meta-path reconstruction loss are jointly trained to obtain graph vectors suitable for the community detection task, which are then used for clustering processing. Experimental results on four datasets (DBLP, ACM, AMiner, and Freebase) show that the model’s NMI and ARI metrics have increased by an average of 3.16% and 3.2% compared with the current state-of-the-art methods. The maximum improvement in the Purity metric reaches 3.71%, and the visualization effect is prominent, which proves the effectiveness of the model.