Link prediction in complex networks using information-theoretic measures

Abstract

Interconnections among real-world entities through explicit or implicit relationships form complex networks, such as social, economic and engineering systems. Recently, the studies based on such complex networks have provided a boost to our understanding of various events and processes ranging from biology to technology. Link prediction algorithms assist in predicting, analysing and deciphering more significant details about the networks and their future structures. In this study, we propose three different link prediction algorithms based on different structural features of the network combined with the information-theoretic analyses. The first two algorithms (variants) are developed for unweighted networks, while the third approach deals with the weighted ones. The proposed methods exhibit better and robust performances in the majority of cases, and at least comparable, if not better in other cases. This work is built upon the previously published mutual information-based approaches for link prediction; however, this study considers structural features of the network to augment mutual information measures and provides insights for finding hidden links in the network.