Keyword aware influential community search in large attributed graphs

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.