Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5102401 | Physica A: Statistical Mechanics and its Applications | 2018 | 20 Pages |
Abstract
Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Shi Chen, Zhi-Zhong Wang, Mei-Hua Bao, Liang Tang, Ji Zhou, Ju Xiang, Jian-Ming Li, Chen-He Yi,