Limitation of multi-resolution methods in community detection

Abstract

Recently, a type of multi-resolution methods in community detection was introduced, which can adjust the resolution of modularity by modifying the modularity function with tunable resolution parameters, such as those proposed by Arenas, Fernandez and Gomez and by Reichardt and Bornholdt. In this paper, we show that these methods still have the intrinsic limitation-large communities may have been split before small communities become visible-because it is at the cost of the community stability that the enhancement of the modularity resolution is obtained. The theoretical results indicated that the limitation depends on the degree of interconnectedness of small communities and the difference between the sizes of small communities and of large communities, while independent of the size of the whole network. These findings have been confirmed in several example networks, where communities even are full-completed sub-graphs.

10 pages, 4 figures

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Cited by in corpus (10)

• Detecting communities using asymptotical Surprise
• Surprise maximization reveals the community structure of complex networks
• Multi-resolution community detection based on generalized self-loop rescaling strategy
• Identifying multi-scale communities in networks by asymptotic surprise
• Global disorder transition in the community structure of large-q Potts systems
• Modularity in Multilayer Networks using Redundancy-based Resolution and Projection-based Inter-Layer Coupling
• Local multiresolution order in community detection
• Router-level community structure of the Internet Autonomous Systems
• Bad Communities with High Modularity
• Dense and sparse vertex connectivity in networks