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The vulnerability of communities in complex network: An entropy approach
Measuring the vulnerability of communities in complex network has become an
important topic in the research of complex system. Numerous existing
vulnerability measures have been proposed to solve such problems, however, most
of these methods have their own shortcomings and limitations. Therefore, a new
entropy-based approach is proposed in this paper to address such problems. This
measure combines the internal factors and external factors for each communities
which can give the quantitative description of vulnerability of community. The
internal factors contain the complexity degree of community and the number of
edges inside the community, and the external factors contain the similarity
degree between chosen community and other communities and the number of nodes
outside the community. Considering community vulnerability from the perspective
of entropy provides a new solution to such problem. Due to sufficient
consideration of community information, more reasonable vulnerability result
can be obtained. In order to show the performance and effectiveness of this
proposed method, one example network and three real-world complex network is
used to compare with some exiting methods, and the sensitivity of weight
factors is analysed by Sobol' indices. The experiment results demonstrate the
reasonableness and superiority of this proposed method