A Heterogeneous Network Community Detection Method Based on GCN and Social Recommendation

To address the issue of sparse and singular connections between users and communities, we propose a novel heterogeneous network community detection method GS-HCD that combines GCN graph convolutional neural network with social recommendation. First, the heterogeneous information network is transformed into a user-user social network Guu and a user-community binary network Guc based on predefined meta paths. The GCN model architecture was adjusted by adding regularization and appropriate activation functions, which achieves the network optimization of Guu and Guc. A labeling mechanism is then introduced to merge the two optimized net-works and construct a user-community extension graph Gcu. Then, in the ex-tended graph Gcu, meta paths that satisfy the criteria are selected between the target user node and the candidate community nodes, and the AvgSim similarity index is used to calculate the similarity based on these meta paths, forming candidate node pairs. Finally, input the vector information of user community candidate nodes into the social recommendation model based on deep learning, learn to capture the dynamic changes of user interests in social networks, and recommend the optimal communities for users. The experimental results on three classical datasets confirm the performance of the GS-HCD model and the accuracy of community detection. Compared with multiple representative methods, the GS-HCD model performs outstandingly in terms of Precision, Recall, and F-score values, and its F1 and AUC values generally exceed the comparative baselines, demonstrating its effectiveness in community detection tasks.