Abstract
Link prediction has attracted increasing research attention recently, which aims to predict missing links in complex networks. However, the existing link prediction methods are primarily based on network structures alone, which are incapable of capturing the dynamics defined on top of the fixed network structures. In this paper, we introduce a linear dynamical response-based similarity measure between nodes into link prediction task. To address the efficiency problem, we design a new iterative procedure to avoid the explicit computation of linear dynamical response (LDR)index. Empirically, we conduct extensive experiments on real networks from various fields. The results show that LDR index leads to promising predicting performance for link prediction.
Original language | English |
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Article number | 121397 |
Journal | Physica A: Statistical Mechanics and its Applications |
Volume | 527 |
DOIs | |
State | Published - Aug 1 2019 |
Bibliographical note
Publisher Copyright:© 2019
Keywords
- Complex networks
- Linear dynamical response
- Link prediction
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability