A Flexible Framework for Query-oriented Interactive Community Search

Community search finds query-dependent communities over graphs, which has been investigated broadly. In this work, we focus on the task of returning only a single connected community containing all user input query vertices. Most existing studies in the literature only propose a single and static model based on a particular subgraph (e.g., 𝑘-core, 𝑘-truss, quasi-clique, and learning-based component). These fixed models are hard to find exact community answers on all datasets and fit with different underlying desires of users and queries. This implies that the community search task needs human-in-loop interactions, which allows users to give feedback and dynamically advise community refinement.

To tackle the above issues, we formulate and study the problem of interactive community search, which allows users to add/delete vertices for improving community answers in a few rounds of interactions. We first summarize dozens of existing community models and develop an integrated notation system M(G,M, O, P) to describe them all. Then, we propose a flexible approach to interactive community search over graphs called GICS-framework. The successful principle of GICS-framework lies on three key components: personalized adding/deleting recommendation, parameter auto-tuning, and fast partial refinement. We develop efficient algorithms and successfully deploy three community models on our GICS-framework. We further analyze algorithm complexity of GICS-framework by illustrating one instance model in detail. Extensive experiments on ground-truth communities demonstrate that our interaction of GICS-framework improves F1-score accuracy by 22% against state-of-the-art competitors, and gives users real-time responses within one second.