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 RA, LP, and CAR 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.
Highlights
In recent years, the research of link prediction in complex networks has captured much attention of researchers from various disciplines [1] because many available real-world networks are incomplete [2, 3] and because link prediction is closely related to many other problems [4, 5]
Since each attribute is associated with a weight in TOPSIS, we present a new algorithm based on the known information about micro nodes to determine the weights
These results demonstrate that LPTOPSIS is invariably in the top place over most networks
Summary
The research of link prediction in complex networks has captured much attention of researchers from various disciplines [1] because many available real-world networks are incomplete [2, 3] and because link prediction is closely related to many other problems [4, 5]. Great efforts have been devoted to link prediction based on observed network structure information [1, 4, 17], such as common neighbors [19,20,21], local paths [22, 23], and triangle structures [24, 25]. Along this line, a plethora of similarity-based indices and methods have been proposed. The prediction performances of these approaches are not stable on different networks
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