Detecting communities around seed nodes in complex networks

The detection of communities (internally dense subgraphs) is a network analysis task with manifold applications. The special task of selective community detection is concerned with finding high-quality communities locally around seed nodes. Given the lack of conclusive experimental studies, we perform a systematic comparison of different previously published as well as novel methods. In particular we evaluate their performance on large complex networks, such as social networks. Algorithms are compared with respect to accuracy in detecting ground truth communities, community quality measures, size of communities and running time. We implement a generic greedy algorithm which subsumes several previous efforts in the field. Experimental evaluation of multiple objective functions and optimizations shows that the frequently proposed greedy approach is not adequate for large datasets. As a more scalable alternative, we propose selSCAN, our adaptation of a global, density-based community detection algorithm. In a novel combination with algebraic distances on graphs, query times can be strongly reduced through preprocessing. However, selSCAN is very sensitive to the choice of numeric parameters, limiting its practicality. The random-walk-based PageRankNibble emerges from the comparison as the most successful candidate.