QUANTITATIVE MEASURE FOR COMMUNITY DETECTION IN WEIGHTED NETWORKS

Detecting communities in weighted networks is becoming a challenging and interesting work. In this paper, a novel quantitative measure called weighted normalized modularity density is proposed and optimized to detect communities in weighted networks. Both theoretical certifications on simple schematic examples and numerical experiments on a suit of simulated networks and real-world networks show that the proposed quantitative measure not only improves the resolution limit in optimizing weighted modularity, but also avoids the emergence of negative communities in optimizing weighted modularity density.

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