IDENTIFY INFLUENTIAL SPREADERS IN COMPLEX NETWORKS BASED ON POTENTIAL EDGE WEIGHTS

It is a key issue to identify influential spreaders in understanding dynamics of information diffusion in complex networks. In existing methods, the edges are treated equally, but each edge has underlying importance and it may be different. In this paper, a novel method called evidential k-shell centrality based on potential edge weight is proposed to identify influential spreaders. First of all, we propose an edge weighting method based on Jaccard similarity for constructing the weighted networks. Secondly, the value of modified evidential centrality is calculated by considering real degree distribution. Thirdly, combining modified evidential centrality and the layer of nodes located in networks, a new method is proposed to identify influential spreaders. Then, in order to evaluate the performance of the proposed method, we adopt the susceptible-infected (SI) model to simulate the epidemic spreading process by using the spreading rate and the number of infected nodes in real complex networks. Experiment results verify that our method is effective for detecting the node influence.

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