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Benchmarking the performance of community detection methods on empiricalsocial network data has been identified as critical for improving thesemethods. In particular, while most current research focuses on detectingcommunities in data that has been digitally extracted from large social mediaand telecommunications services, most evaluation of this research is based onsmall, hand-curated datasets. We argue that these two types of networks differso significantly that by evaluating algorithms solely on the former, we knowlittle about how well they perform on the latter. To address this problem, weconsider the difficulties that arise in constructing benchmarks based ondigitally extracted network data, and propose a task-based strategy which wefeel addresses these difficulties. To demonstrate that our scheme is effective,we use it to carry out a substantial benchmark based on Facebook data. Thebenchmark reveals that some of the most popular algorithms fail to detectfine-grained community structure.

Benchmarking community detection methods on social media data