In view that the K-shell decomposition method can only effectively identify a single most influential node, but cannot accurately identify a group of most influential nodes, this article proposes a hybrid method based on K-shell decomposition to identify the most influential spreaders in complex networks. First, the K-shell decomposition method is used to decompose the network, and the network is regarded as a hierarchical structure from the inner core to the periphery core. Second, the existing centrality methods such as H-index are used as the secondary score of the proposed method to select nodes in each hierarchy of the network. In addition, for the sake of alleviating the overlapping problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced to calculate the comprehensive score of secondary score and overlapping range, and the node with the highest comprehensive score will be selected in each round. The proposed algorithm can be used as a general framework to improve the existing centrality method which can represent nodes with definite values of centrality. Experimental results show that in the susceptible-infected-recovered (SIR) model experiment, compared with the benchmark methods, the infection scale of the proposed K-TOPSIS method in nine real networks is improved by 1.15%, 2.23%, 1.95%, 3.12%, 6.29%, -0.37%, 4.01%, 0.48%, and 0.48%, respectively. The novel method is improved by 0.44, 1.18, 1.16, 11.30, 2.03, 2.53, 2.70, and 2.13 in average shortest path length experiment, respectively, except for Facebook network. It shows that the novel method is reasonable and effective.

Influential Spreaders Identification in Complex Networks With TOPSIS and K-Shell Decomposition

Fiumara, G;De Meo, P
2022-01-01

Abstract

In view that the K-shell decomposition method can only effectively identify a single most influential node, but cannot accurately identify a group of most influential nodes, this article proposes a hybrid method based on K-shell decomposition to identify the most influential spreaders in complex networks. First, the K-shell decomposition method is used to decompose the network, and the network is regarded as a hierarchical structure from the inner core to the periphery core. Second, the existing centrality methods such as H-index are used as the secondary score of the proposed method to select nodes in each hierarchy of the network. In addition, for the sake of alleviating the overlapping problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced to calculate the comprehensive score of secondary score and overlapping range, and the node with the highest comprehensive score will be selected in each round. The proposed algorithm can be used as a general framework to improve the existing centrality method which can represent nodes with definite values of centrality. Experimental results show that in the susceptible-infected-recovered (SIR) model experiment, compared with the benchmark methods, the infection scale of the proposed K-TOPSIS method in nine real networks is improved by 1.15%, 2.23%, 1.95%, 3.12%, 6.29%, -0.37%, 4.01%, 0.48%, and 0.48%, respectively. The novel method is improved by 0.44, 1.18, 1.16, 11.30, 2.03, 2.53, 2.70, and 2.13 in average shortest path length experiment, respectively, except for Facebook network. It shows that the novel method is reasonable and effective.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3230389
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