中国物理B ›› 2021, Vol. 30 ›› Issue (8): 88902-088902.doi: 10.1088/1674-1056/abff2d
• • 上一篇
Detection of influential nodes with multi-scale information
Jing-En Wang(王静恩)1,†, San-Yang Liu(刘三阳)1, Ahmed Aljmiai2, and Yi-Guang Bai(白艺光)1,‡
- 1 School of Mathematics and Statistics, Xidian University, Xi'an 710071, China;
2 David R. Cheriton School of Computer Science, University of Waterloo, Canada
Detection of influential nodes with multi-scale information
Jing-En Wang(王静恩)1,†, San-Yang Liu(刘三阳)1, Ahmed Aljmiai2, and Yi-Guang Bai(白艺光)1,‡
- 1 School of Mathematics and Statistics, Xidian University, Xi'an 710071, China;
2 David R. Cheriton School of Computer Science, University of Waterloo, Canada
摘要： The identification of influential nodes in complex networks is one of the most exciting topics in network science. The latest work successfully compares each node using local connectivity and weak tie theory from a new perspective. We study the structural properties of networks in depth and extend this successful node evaluation from single-scale to multi-scale. In particular, one novel position parameter based on node transmission efficiency is proposed, which mainly depends on the shortest distances from target nodes to high-degree nodes. In this regard, the novel multi-scale information importance (MSII) method is proposed to better identify the crucial nodes by combining the network's local connectivity and global position information. In simulation comparisons, five state-of-the-art algorithms, i.e. the neighbor nodes degree algorithm (NND), betweenness centrality, closeness centrality, Katz centrality and the k-shell decomposition method, are selected to compare with our MSII. The results demonstrate that our method obtains superior performance in terms of robustness and spreading propagation for both real-world and artificial networks.
中图分类号: (Structures and organization in complex systems)
- 89.75.Fb