CDA: A clustering degree based influential spreader identification algorithm in weighted complex network

Wang, Qian; Ren, Jiadong; Wang, Yu; Zhang, Bing; Cheng, Yongqiang; Zhao, Xiaolin

Abstract

Identifying the most influential spreaders in a weighted complex network is vital for optimizing utilization of the network structure and promoting the information propagation. Most existing algorithms focus on node centrality, which consider more connectivity than clustering. In this paper, a novel algorithm based on clustering degree algorithm (CDA) is proposed to identify the most influential spreaders in a weighted network. First, the weighted degree of a node is defined according to the node degree and strength. Then, based on the node weighted degree, the clustering degree of a node is calculated in respect to the network topological structure. Finally, the propagation capability of a node is achieved by accounting the clustering degree of the node and the contribution from its neighbors. In order to evaluate the performance of the proposed CDA algorithm, the susceptible-infected-recovered model is adopted to simulate the propagation process in real-world networks. The experiment results have showed that CDA is the most effective algorithm in terms of Kendall's tau coefficient and with the highest accuracy in influential spreader identification compared with other algorithms such as weighted degree centrality, weighted closeness centrality, evidential centrality, and evidential semilocal centrality.

Citation

Wang, Q., Ren, J., Wang, Y., Zhang, B., Cheng, Y., & Zhao, X. (2018). CDA: A clustering degree based influential spreader identification algorithm in weighted complex network. IEEE Access, 6, 19550-19559. https://doi.org/10.1109/ACCESS.2018.2822844

• Journal Article Type: Article
• Acceptance Date: Feb 15, 2018
• Publication Date: Apr 4, 2018
• Deposit Date: Jul 19, 2018
• Publicly Available Date: Jul 20, 2018
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Pages: 19550-19559
• DOI: https://doi.org/10.1109/ACCESS.2018.2822844
• Keywords: Clustering algorithms; Complex networks; Software; Weight measurement; Tuning; Software algorithms; Indexes
• Public URL: https://hull-repository.worktribe.com/output/937051
• Publisher URL: https://ieeexplore.ieee.org/document/8331080/
• Contract Date: Jul 20, 2018