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Improved community evolution relationship analysis method for dynamic graphs
LUO Xiangyu, LI Jianan, LUO Xiaoxia, WANG Jia
Journal of Computer Applications
2020, 40 (8):
2313-2318.
DOI: 10.11772/j.issn.1001-9081.2020010072
The community evolution relationships extracted by the traditional adjacent time slice analysis cannot fully describe the entire community evolution process in dynamic graphs. Therefore, an improved community evolution relationship analysis method was proposed. First, the community events were defined, and the evolution states of the community were described according to the occurred community events. Then, the event matching was performed on two communities within different time slices to obtain community evolution relationships. Results of comparison with the traditional methods show that the total number of community events detected by the proposed method is more than twice that revealed by the traditional method, which proves that the proposed method can provide more useful information for describing the evolution process of communities in dynamic graphs.
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