Chen H.-WShuai H.-HYang D.-NLee W.-CShi CYu P.SMING-SYAN CHEN2022-04-252022-04-25202110844627https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112868201&doi=10.1109%2fICDE51399.2021.00203&partnerID=40&md5=ad261f282a8a341345c865036f79bb09https://scholars.lib.ntu.edu.tw/handle/123456789/607258Owing to a wide range of important applications, such as team formation, dense subgraph discovery, and activity attendee suggestions on online social networks, Group Query attracts a lot of attention from the research community. However, most existing works are constrained by a unified social tightness k (e.g., for k-core, or k-plex), without considering the diverse preferences of social cohesiveness in individuals. In this paper, we introduce a new group query, namely Parameter-free Group Query (PGQ), and propose a learning-based model, called PGQN, to find a group that accommodates personalized requirements on social contexts and activity topics. First, PGQN extracts node features by a GNN-based method on Heterogeneous Activity Information Network (HAIN). Then, we transform the PGQ into a graph-to-set (Graph2Set) problem to learn the diverse user preference on topics and members, and find new attendees to the group. Experimental results manifest that our proposed model outperforms nine state-of-the-art methods by at least 51% in terms of F1-score on three public datasets. ? 2021 IEEE.Graph neural networkGroup querHINTransformerSocial networking (online)Activity informationsHeterogeneous informationLearning Based ModelsOn-line social networksResearch communitiesState-of-the-art methodsStructure-awareTeam formationInformation services[SDGs]SDG4conference paper10.1109/ICDE51399.2021.002032-s2.0-85112868201