Identifying and Ranking Influential Spreaders in Complex Networks by Localized Decreasing Gravity Model

Author:

Xiang Nan123,Tang Xiao1,Liu Huiling1,Ma Xiaoxia1

Affiliation:

1. Liang Jiang International College, Chongqing University of Technology , No. 459 Pufu Avenue, Longxing Town, Yubei District, Chongqing 401135 , China

2. College of Computer Science, Chongqing University , No. 174 Shazheng Street, Shapingba District, Chongqing 400044 , China

3. All-Terrain Vehicle Research Institute, Chongqing Jialing Special Equipment Co., Ltd , No. 100 Shuangbei Free Village, Shapingba District, Chongqing 400032 , China

Abstract

Abstract Identifying crucial nodes in complex networks is paid more attention in recent years. Some classical methods, such as degree centrality, betweenness centrality and closeness centrality, have their advantages and disadvantages. Recently, the gravity model is applied to describe the relationship of nodes in a complex network. However, the interaction force in gravity model follows the square law of distance, which is inconsistent with the actual situation. Most people are generally affected by those who are surrounding them, which means that local influence should be emphasized. To address this issue, we propose an indexing method called localized decreasing gravity centrality by maximizing the local influence of a node. In the proposed measure, the mass and radius of gravity model are redefined, which can represent the spreading ability of the node. In addition, a decreasing weight is added to strengthen the local influence of a node. To evaluate the performance of the proposed method, we utilize four different types of networks, including interaction networks, economic networks, collaboration networks and animal social networks. Also, two different infectious disease models, susceptible-infectious-recovered (SIR) and susceptible-exposed-low risk-high risk-recovered (SELHR), are utilized to examine the spreading ability of influential nodes.

Funder

Natural Science Foundation of Chongqing Province of China

China Postdoctoral Science Foundation

The Science and Technology Research Project of Chongqing Education Commission

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

General Computer Science

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