Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 206, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2022.117791
Keywords
Complex network; Influential spreaders; Spreading ability; Semi-global triangular centrality; SIR epidemic model
Categories
Funding
- Meity Govt. of India
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This article classifies the methods for identifying influential spreaders into four categories: local centrality, global centrality, semi-global centrality, and hybrid centrality. Semi-global centrality based methods are found to be highly effective in identifying influential spreaders from different network structures. However, existing methods tend to overlook the spreaders from the periphery of a network. To address this issue, the authors propose a new indexing method called semiglobal triangular centrality, which focuses on selecting the best spreaders from the dense part of a network. Experimental results show that this method outperforms other centrality methods in terms of spreading dynamics.
The influential spreaders play a significant role in maximizing or controlling any spreading process in a network. In the literature, many methods have been proposed to identify influential spreaders. In this article, we classify all the methods mainly into four categories, such as local centrality, global centrality, semi-global centrality and hybrid centrality. Among them, we have found semi-global centrality based methods have immense potential in identifying the influential spreaders from various types of network structures. However, we have observed that the existing semi-global centrality methods can identify the spreaders from the periphery of a network, where the nodes in the periphery are loosely coupled and the collective influence in the peripheral region of a spreading process will be nominal. We propose a new indexing method semiglobal triangular centrality, which does not consider the best spreaders from the periphery. The proposed method maximizes the total collective influence of a spreading process by selecting the best spreaders from the dense part of a network. We have examined the performance of the proposed method using the Susceptible-Infected-Recovered epidemic model and applied to nine real-networks. The experimental result reveals that the proposed method performs better than the other centrality methods in terms of spreading dynamics.
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