Abstract
The identification of critical nodes in complex networks is an open issue. Many scholars have tried to address it from different perspectives, but their methods are often not as effective as usual especially when meeting some specific graphs or limited to only one aspect. Evidence theory can consider the results from different sources comprehensively and the Shannon entropy can measure the uncertainty of information. In this paper, we use these two methods to rate the results gained from different measures and combine them to generate a new ranking result, namely Evidence Theory Centrality (ETC). The Susceptible-infected (SI) model and Kendall's tau coefficient are used on six real networks to examine the effectiveness of our method.
Highlights
With the explosion of data and the rise of the Internet, complex network received much attention in many fields [1]–[10], such as time series [11], [12], link prediction [13], [14] and computer science [15]
The Degree centrality (DC), Betweenness centrality (BC), and Closeness centrality (CC) were selected as participating in the fusion
Rather than specifying some fixed methods to fuse, our method provides a flexible framework for selecting the required method to fuse according to the actual situation
Summary
With the explosion of data and the rise of the Internet, complex network received much attention in many fields [1]–[10], such as time series [11], [12], link prediction [13], [14] and computer science [15]. Many methods have been proposed to identify the influential nodes in complex networks [33]–[41]. Kitsak et al [44] hold that the location of a node plays the more important role, so the K-Shell was proposed. Wei et al [52] proposed the EVC (Evidence centrality), which has tried to solve the problem of identifying important nodes with evidence theory, but it is only limited to the local characteristics of nodes. We abstract the ranking values of different methods into BPA (Basic Probability Assignment) and use Dempster-Shafer theory evidence theory to combine them.
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