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 ques-tion answering (CQA) platforms have formed mature communities by manually marked topics and extensive accumulated user behavior. However, the ever-growing various enti-ties 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 infor-mation network to uniformly represent all connections. Afterward, we exploit the hetero-geneous 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 clus-tering task in the heterogeneous information network and improve the k-means method to achieve heterogeneous community detection. Based on our knowledge of the existing lit-erature, 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.(c) 2022 Elsevier Inc. All rights reserved.