CommunityAF: An Example-Based Community Search Method via Autoregressive Flow

Example-based community search utilizes hidden patterns of given examples rather than explicit rules, reducing users' burden and enhancing flexibility. However, existing works face challenges such as low scalability, high training cost, and improper termination during the search. Aiming at tackling all these issues, this paper proposes a community search framework named CommunityAF with three well-designed components. The first is a GNN (graph neural network) component that combines community-aware structure features to incrementally learn node embeddings over a large graph for the other two components. The second is an autoregres-sive flow-based generation component designed for fast training and model stability. The third is a scoring component that evaluates the communities and provides scores for a stable termination. Moreover, to show that CommunityAF has the sufficient expressive power to cover the rules, we demonstrate that the scoring component with node features weighted by degree-related factors is able to mimic the existing structure-based community metrics. We introduce a square ranking loss to guide the training of the scoring component, and further devise a flexible termination strategy based on the inferred score change pattern over a sequence of candidate communities using beam search. We compare CommunityAF with four different categories of community search methods on six real-world datasets. The results illustrate that CommunityAF outperforms these community search methods, and achieves an average 15.3% improvement in effectiveness and 4x to 20x speedups on different datasets relative to the state-of-the-art generative method.