Ami Hauptman
ABSTRACT
One important aspect of graphs representing complex systems is community (or group) structure---assigning vertices to groups, which have dense intra-group connections and relatively sparse inter-group connections. Community detection is of great importance in various domains, where real-world complex systems are represented as graphs, since communities facilitate our understanding of the graph and thus of the underlying system. However, this is known to be a hard optimization problem.
In this study we pursue the following question: Have Evolutionary Computation-Based Methods proven their worth for this complex domain, or is it currently better to rely on other state-of-the-art methods? While several works compare state-of-the-art methods for community detection (see [8] and [11] for recent surveys), we are unaware of other attempts to compare methods based on evolutionary computation to other methods.
After describing some recent algorithms for this problem, and comparing them in various ways, we conclude that evolutionary computation-based method for community detection have indeed developed to hold their own against other methods for several variants of this problem. However, they still need to be applied to more difficult problems and improve further to make them in par with other methods.
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Digital Library
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Index Terms
- Are Evolutionary Computation-Based Methods Comparable to State-of-the-art non-Evolutionary Methods for Community Detection?
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