Community Detection via Maximization of Modularity and Its Variants

In this paper, we first discuss the definition of modularity (Q) used as a metric for community quality and then we review the modularity maximization approaches which were used for community detection in the last decade. Then, we discuss two opposite yet coexisting problems of modularity optimization; in some cases, it tends to favor small communities over large ones while in others, large communities over small ones (so-called the resolution limit problem). Next, we overview several community quality metrics proposed to solve the resolution limit problem and discuss Modularity Density (Qds ), which simultaneously avoids the two problems of modularity. Finally, we introduce two novel fine-tuned community detection algorithms that iteratively attempt to improve the community quality measurements by splitting and merging the given network community structure. The first one, referred to as Fine-tuned , is based on modularity (Q), while the second is based on Modularity Density (Qds ) and denoted as Fine-tuned . Then, we compare the greedy algorithm of modularity maximization (denoted as Greedy ), Fine-tuned , and Fine-tuned on four real networks, and also on the classical clique network and the LFR benchmark networks, each of which is instantiated by a wide range of parameters. The results indicate that Fine-tuned Qds is the most effective among the three algorithms discussed. Moreover, we show that Fine-tuned Qds can be applied to the communities detected by other algorithms to significantly improve their results.

• Publication year

  2014

• Venue

  IEEE Transactions on Computational Social Systems

• Publication date

  2014-04-09

• Fields of study

  Mathematics, Physics, Computer Science

• Identifiers
  DOI 10.1109/TCSS.2014.2307458arXiv 1507.00787
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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