Heterogeneous link prediction based on multi relational community information

Social networks consisting of edges annotated with multiple links are natural models for real-world networks and pose a challenge for network analysis. Link prediction, predicting future links or missing links in a multi-relational network, is an important task from applications perspective. In large networks, time and memory are major constraints for link prediction. In this context, an algorithm is proposed to improve upon the recent solutions proposed for this problem. In this paper, a parallel method for predicting links in heterogeneous networks is proposed. As social networks exhibit a natural community structure and the nodes interact more within community than with the nodes in other communities, this multi relational community information is used for parallelization. Utilizing the existing state-of-the art algorithms for multi-relational link prediction as well as community discovery algorithms, the proposed method, computes multi relational link prediction scores in each community. The results of implementation of these algorithms on bench-mark data sets show that community information does significantly help in improving the performance of multi-relational link prediction.