| Research on dynamic temporal networks has made considerable progress. However, Current researches on temporal networks mainly tend to detect community structure. A number of community detection algorithms can obtain community structure on each time slice or each period of time, but rarely present the evolution of community structure. Some papers discussed the process of community structure evolution but lacked quantifying the evolution.Based on the above analysis, here we put forward the concept of Community Vitality, which shows a community’s life intensity on a time slice and can be used to describe communities evolution over time quantitatively. On the one hand, when compared a community vitality on a time slice with that on last time slice, the community’s structure in a state of growth or disappearance can be understood; on the other hand, compared with other community detection algorithms, the “dead communities” and the communities in the state of growth can be distinguished by analyzing the changing process of a community’s community vitality on its lifecycle. Furthermore, community vitality change rate is proposed for revealing communities’ structure change.Evolution process of the community can be forecast by utilizing that the result of communities evolution calculation community vitality, which can be to optimize self-organization communication networks and analyze viruses propagation. Therefore, the Incremental Virus Immune Strategy is put forward and used for dynamic nature of dynamic temporal networks. Furthermore, by creating theoretical model to analyze the immunity of the immunization strategy.In the end, two real datasets(Chinese DBLP dataset and Enron Email dataset) are used to do experiments. The results of our experiments show that community vitality is a novel and effective way to understand or model the community evolution by using the concept Community Vitality. Furthermore, comparing with Random Immunization and Targeted Immunization, the immunity of the Incremental Virus Immune Strategy based on Community Vitality has superiority, also some correlation is found out. |
