Enhancement to community-based multi-relational link prediction using co-occurrence probability feature

Abstract

Predicting future links or missing links is one of the useful application tasks in the analysis of social networks. Time and memory are major challenges for the link prediction task in large multirelational social networks. This challenge is addressed in this paper, by proposing a parallel method for link prediction. Community information is used for parallelization since social networks tend to form natural communities, and probability of intra community node interaction is much more than inter community interaction. For prediction task, along with the standard topological features, the recently proposed local probabilistic graph model is also used. This model infers the joint co-occurrence probability of two nodes (i; j) from Markov Random Field constructed using the nodes in the neighbourhood of (i; j). In this paper, we adopt the supervised framework of MR-HPLP of the literature by including the co-occurrence probability feature in the multi relational environment, and reducing the dimensionality of the feature vector. This method is evaluated on a challenging benchmark multi relational dataset and COP is shown to outperform as an unsupervised predictor. Further MR-HPLP-COP shows significant improvement in AUROC as well as AUPR scores over all the ten existing predictors on the benchmark data set. In particular, MR-HPLP-COP shows AUROC of over 90% for two data sets for which the existing methods give a prediction performance of around 75%.