A new study of using temporality and weights to improve similarity measures for link prediction of social networks

Abstract

Link prediction is the problem of inferring future interactions among existing network members based on available knowledge. Computing similarity between a node pair is a known solution for link prediction. This article proposes some new similarity measures. Some of them use nodes' recency of activities, some weights of edges and some fusion of both in their calculation. A new definition of recency is provided here. A supervised learning method that applies a range of network properties and nodes similarity measures as its features set is developed here for experiments. The results of the experiments indicate that using proposed similarity measures would improve the performance of the link prediction. © 2018 - IOS Press and the authors. All rights reserved.