Open AccessMutual information model for link prediction in heterogeneous complex networks
Author(s) -
Hadi Shakibian,
Nasrollah Moghadam Charkari
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep44981
Subject(s) - computer science , link (geometry) , mutual information , similarity (geometry) , node (physics) , path (computing) , entropy (arrow of time) , random walk , data mining , complex network , set (abstract data type) , artificial intelligence , mathematics , statistics , computer network , physics , world wide web , engineering , image (mathematics) , programming language , structural engineering , quantum mechanics
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.