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- Title
基于共同邻居邻域拓扑稠密性加权的链路预测方法.
- Authors
李星; 朱宇航; 柏溢; 李劲松
- Abstract
Link prediction aims to use known network nodes and topology information to predict the possibility of edges between two unconnected nodes in the network. The link prediction method based on network topological similarity has low computational complexity and good prediction effect, but the existing similarity indices take less consideration of the neighborhood topological information of common neighbors. To solve this problem, this paper proposed a link prediction method based on the weighted neighborhood topological denseness of the common neighbor. First, the method quantified the neighborhood topology of nodes based on the relative density index of neighborhood topology. Then, the node degree of the common neighbor and the relative density index of the neighborhood topology were used to describe the similarity contributions of the common neighbor and its surrounding topology. Finally, a node similarity index based on the weighting of the topological denseness of common neighbors was proposed. The experimental results on multiple actual networks show that the proposed method can achieve higher prediction accuracy compared with existing similarity indices.
- Subjects
SPECIFIC gravity; PROBLEM solving; COMPUTATIONAL complexity; NEIGHBORHOODS; TOPOLOGY; SIMILARITY (Geometry)
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2021, Vol 38, Issue 5, p1503
- ISSN
1001-3695
- Publication type
Article
- DOI
10.19734/j.issn.1001-3695.2020.06.0190