Inferring Future Links in Large Scale Networks

Abstract

The challenge in predicting future links over large scale networks (social networks) is not only maintaining accuracy, but also coping with the time-varying network graph. In contrast to the existing approaches, in this work we propose building a Markov prediction model. It not only incorporates temporal snapshots reflecting the dynamic network graph, but also considers effect of multiple timescales, along with corresponding local and global structural evolution (links and clusters respectively), correlated evolution and rate of evolution. The resulting edge selection in our approach exhibits the power law degree distribution, as exhibited in real world networks. Finally, we use two heavily dynamic real world network temporal data set (e.g. Twitter and Enron) and one relatively less dynamic network data set (e.g. DBLP), and existing state-of-the-art static and recent dynamic measures, to evaluate the prediction accuracy of our proposed Markov model and show that it out performs existing approaches.