Heterogeneous question answering community detection based on graph neural network

Topic-based communities have gradually become a considerable medium for netizens to disseminate and acquire knowledge. These communities consist of entities (actual objects, e.g., a real answer or an actual question) with different types (users, questions and answers) and are usually hidden and overlapping. Nowadays, prevalent community question answering (CQA) platforms have formed mature communities by manually marked topics and extensive accumulated user behavior. However, the ever-growing various entities and complex overlapping topic communities make it inefficient to manually label entity tags (e.g., Question labels supplement domain features; Potential user tags indicate the user's specialty.). Therefore, there is an urgent need for a mechanism that automatically finds hidden semantic communities from user social behavior and lays a foundation for community construction and intelligent recommendation of QA platforms. In this paper, we propose a Heterogeneous Community Detection Approach Based on Graph Neural Network, called HCDBG, to detect heterogeneous communities in CQA. Firstly, we define entity relationships based on user interaction behavior and employ a heterogeneous information network to uniformly represent all connections. Afterward, we exploit the heterogeneous graph neural network to fuse content and topological features of nodes for graph embedding. Finally, we convert the community detection issue in CQA into an entity clustering task in the heterogeneous information network and improve the k-means method to achieve heterogeneous community detection. Based on our knowledge of the existing literature, it is an innovative research direction that utilizes the heterogeneous graph neural network to facilitate QA community detection. Extensive experiments on authentic question-answering datasets illustrate that HCDBG outperforms baseline methods in heterogeneous community detection.