Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method. (2nd September 2018)

• Record Type: Journal Article
• Authors: Li, Longjie; Bai, Shenshen; Leng, Mingwei; Wang, Lu; Chen, Xiaoyun
• Other Names: Caldarelli Guido Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-09-02
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/3579758
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider R A, L P, and C A R indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.