Strengthening the Common Neighbor Algorithm by
the Degree of Importance of Nodes for Predicting
Missing Links in Complex Networks
Mourad Charikhi
Department of Computer Science
Mohamed El Bachir El Ibrahimi University
El-Anasser
34030, Bordj Bou Arreridj, Algeria
Abstract
Social and complex networks analyses are important areas of research. Link prediction is a discipline in this field that attempts to find missing links and predict new links in networks. Node proximity approaches are widely used in link prediction problems; however, they suffer from weak performance. We propose a new method based on node proximity that exploits common neighbors and the degree of importance of nodes using the well-known PageRank algorithm. Our method improves the performance of existing local methods with a low computation time. Experiments conducted on nine datasets show the advantage of our new method compared to the basic methods of common neighbors, Adamic–Adar and resource allocation. We compare our approach and 14 state-of-the-art techniques, including path and local information approaches. The results indicate that our new approach provides a significant improvement in terms of area under the receiver operating characteristic curve (AUC) score with linear computational complexity.
Keywords: link prediction; similarity metrics; PageRank algorithm; network evolution; social network
Cite this publication as:
M. Charikhi, “Strengthening the Common Neighbor Algorithm by the Degree of Importance of Nodes for Predicting Missing Links in Complex Networks,” Complex Systems, 35(2), 2026 pp. 181–202.
https://doi.org/10.25088/ComplexSystems.35.2.181
