Keyword aware influential community search in large attributed graphs

Md. Saiful Islam, Mohammed Eunus Ali, Yong-Bin Kang, Timos Sellis, Farhana Murtaza Choudhury, Shamik Roy

Abstract: Highlights • The query considers user preference (as keywords) for influential community search. • Considering the semantics of the keywords adds flexibility for the users. • The proposed influence measure eliminates the need for internal parameters as input. • A novel index is proposed to efficiently retrieve the most influential communities. • The datasets are shared publicly to help future works in this direction. Abstract Influential community search (ICS) on a graph finds a closely connected group of vertices having a dominance over other groups of vertices. The ICS has many applications in recommendations, event organization, and so on. In this paper, we introduce a new variant of ICS, namely keyword-aware influential community query (KICQ), that finds the communities with the highest influential scores and whose keywords match with the query terms (a set of keywords) and predicates (AND or OR). It is challenging to find such communities from a large network as the traditional pre-computation approach is not applicable with the change of query terms at every instance of the search. To solve this problem, we design two efficient algorithms: (i) a branch-and-bound approach that exploits the bounds computed from already explored communities to prune the search space, and (ii) a novel index based approach that hierarchically organizes sub-communities and keywords with associated bounds to quickly identify the desired communities. We propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.