Detecting community structure discloses tremendous information about complex networks and unlock promising applied perspectives. Accordingly, a numerous number of community detection methods have been proposed in the last two decades with many rewarding discoveries. Notwithstanding, it is still very challenging to determine a suitable method in order to get more insights into the mesoscopic structure of a network given an expected quality, especially on large scale networks. Many recent efforts have also been devoted to investigating various qualities of community structure associated with detection methods, but the answer to this question is still very far from being straightforward. In this paper, we propose a novel approach to estimate the similarity between community detection methods using the size density distributions of communities that they detect. We verify our solution on a very large corpus of networks consisting in more than a hundred networks of five different categories and deliver pairwise similarities of 16 state-of-the-art and well-known methods. Interestingly, our result shows that there is a very clear distinction between the partitioning strategies of different community detection methods. This distinction plays an important role in assisting network analysts to identify their rule-of-thumb solutions.
International audienceCommunity detection emerged as an important exploratory task in complex networks analysis across many scientific domains. Many methods have been proposed to solve this problem, each one with its own mechanism and sometimes with a different notion of community. In this article, we bring most common methods in the literature together in a comparative approach and reveal their performances in both real-world networks and synthetic networks. Surprisingly, many of those methods discovered better communities than the declared ground-truth communities in terms of some topological goodness features, even on benchmarking networks with built-in communities. We illustrate different structural characteristics that these methods could identify in order to support users to choose an appropriate method according to their specific requirements on different structural qualities
Community detection emerges as an important task in the discovery of network mesoscopic structures. However, the concept of a "good" community is very context-dependent and it is relatively complicated to deduce community characteristics using available community detection techniques. In reality, the existence of a gap between structural goodness quality metrics and expected topological patterns creates a confusion in evaluating community structures. In this paper, we introduce an empirical multivariate analysis of different structural goodness properties in order to characterize several detectable community topologies. Specifically, we show that a combination of two representative structural dimensions including community transitivity and hub dominance allows to distinguish different topologies such as star-based, clique-based, string-based and grid-based structures. Additionally, these classes of topology disclose structural proximities with those of graphs created by Erdős-Rényi, Watts-Strogatz and Barabási-Albert generative models. We illustrate popular community topologies identified by different detection methods on a large dataset composing many network categories and associate their structures with the most related graph generative model. Interestingly, this conjunctive representation sheds light on fundamental differences between mesoscopic structures in various network categories including: communication, information, biological, technological, social, ecological, synthetic networks and more.
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