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Abstract
Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.
Highlights
A number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks
The aim of link prediction is to exploit dependencies between any node pairs[1]. It has many real-world applications like friend recommendation in social networks[2], detecting selfish or spurious nodes/edges in social networks[3], citation predicting in scientific collaboration networks[4], modeling the evolution of complex networks[5], etc
Common Neighbors (CN), Jaccard (JC), Prefrential Attachment (PA), Adamic Adar (AA), and Resource Allocation (RA) are among popular local indices, while Katz, Leicht-Holme-Newman, Average Commute Time, Random Walk, and SimRank are known as global indices[6]
Summary
A number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. These indices suffer from two major drawbacks. Employing a set of different meta-paths is not straightforward To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The majority of link prediction approaches have been proposed on homogenous complex networks They are divided into some categories as local/global similarity indices, supervised, and probabilistic methods. An excellent survey on these categories can be found in ref. 6
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