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Local community detection, the problem of identifying a set of relevant nodesnearby a small set of input seed nodes, is an important graph primitive with awealth of applications and research activity. Recent approaches include usinglocal spectral information, graph diffusions, and random walks to determine acommunity from input seeds. As networks grow to billions of nodes and exhibitdiverse structures, it is important that community detection algorithms are notonly efficient, but also robust to different structural features.  Toward this goal, we explore pre-processing techniques and modifications toexisting local methods aimed at improving the scalability and robustness ofalgorithms related to community detection. Experiments show that ourmodifications improve both speed and quality of existing methods for locatingground truth communities, and are more robust across graphs and communities ofvarying sizes, densities, and diameters. Our subgraph extraction method usesadaptively selected PageRank parameters to improve on the recall and runtime ofa walk-based pre-processing technique of Li et al. for extracting subgraphsbefore searching for a community. We then use this technique to enable thefirst scalable implementation of the recent Local Fiedler method of Mahoney etal. Our experimental evaluation shows our pre-processed version of LocalFiedler, as well as our novel simplification of the LEMON community detectionframework of Li et al., offer significant speedups over their predecessors andobtain cluster quality competitive with the state of the art.

Scalable and Robust Local Community Detection via Adaptive Subgraph Extraction and Diffusions