Published May 2016 | Version v1
Journal article

Identifying influential spreaders by weight degree centrality in complex networks

  • 1. School of Computer and Information Science, Southwest University, Chongqing 400715 (China)
  • 2. Institute of Intelligent Control and systems, Harbin Institute of Technology, Harbin 150080 (China)
  • 3. School of Electronics and Information, Northwestern Polytechnical University, Xian, Shaanxi, 710072 (China)

Description

The problem of identifying influential spreaders in complex networks has attracted much attention because of its great theoretical significance and wide application. In this paper, we propose a successful ranking method for identifying the influential spreaders. The proposed method measures the spreading ability of nodes based on their degree and their ability of spreading out. We also use a tuning weight parameter, which is always associated with the property of the networks such as the assortativity, to regulate the weight between the degree and the ability of spreading out. To test the effectiveness of the proposed method, we conduct the experiments on several synthetic networks and real-world networks. The results show that the proposed method outperforms the existing well-known ranking methods.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.chaos.2016.01.030

Additional details

Identifiers

DOI
10.1016/j.chaos.2016.01.030;
PII
S0960-0779(16)30021-2;

Publishing Information

Journal Title
Chaos, Solitons and Fractals
Journal Volume
86
Journal Page Range
p. 1-7
ISSN
0960-0779

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
48001889
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
MATHEMATICAL MODELS; NETWORK ANALYSIS; TUNING; WEIGHT

Optional Information

Copyright
Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.