Community detection: effective evaluation on large social networks
|Title:||Community detection: effective evaluation on large social networks||Authors:||Lee, Conrad
|Permanent link:||http://hdl.handle.net/10197/8533||Date:||2014||Abstract:||While many recently proposed methods aim to detect network communities in large datasets, such as those generated by social media and telecommunications services, most evaluation (i.e. benchmarking) of this research is based on small, hand-curated datasets. We argue that these two types of networks differ so significantly that, by evaluating algorithms solely on the smaller networks, we know little about how well they perform on the larger datasets. Recent work addresses this problem by introducing social network datasets annotated with meta-data that is believed to approximately indicate a 'ground truth' set of network communities. While such efforts are a step in the right direction, we find this meta-data problematic for two reasons. First, in practice, the groups contained in such meta-data may only be a subset of a network’s communities. Second, while it is often reasonable to assume that meta-data is related to network communities in some way, we must be cautious about assuming that these groups correspond closely to network communities. Here, we consider these difficulties and propose an evaluation scheme based on a classification task that is tailored to deal with them.||Type of material:||Journal Article||Publisher:||Oxford University Press||Copyright (published version):||2013 the Authors||Keywords:||Machine learning; Statistics; Social networks; Community detection; Evaluation; Benchmarking||DOI:||10.1093/comnet/cnt012||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
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