Incorporating User Preference for Community Search and Outlier Detection in Attributed Network

Community search aims to search local communities containing query nodes, which is one of the most concerned studies in network analysis task. Most existing community search methods are oriented to simple network and can only detect the community where query nodes are located. They may fail to take user's preferences into account during searching process. To guide the process of community search via user's preferences for finding multi-communities that us⁃ ers are interested in, we propose a community search method that is capable of searching multi-communities with user's preference and simultaneously identify outliers via few given query nodes in attributed network. Clearly, we explicitly mod⁃ el interactions between query nodes with its neighbors and encode similar attributes to highlight the local structure, which could be beneficial for query nodes to mine potential candidates. And we define the average partition similarity on candi⁃ date set of query nodes to infer attribute subspace as user's latent interest. Multi-communities and outliers in the whole net⁃ work are detected via fractional-core and structural constraints. Experiments on real and synthetic network datasets demon⁃ strate the effectiveness of the proposed algorithm. © 2022 Chinese Institute of Electronics. All rights reserved.