Link Prediction in Heterogeneous Networks: Influence and Time Matters
Link prediction is an important task in network analysis, benefiting researchers and organizations in a variety of fields. Many networks in the real world, for example social networks, are heterogeneous, having multiple types of links and complex dependence structures. Link prediction in such networks must model the influence propagating between heterogeneous relationships to achieve better link prediction performance than in homogeneous networks. In this paper, the authors introduce Multi-Relational Influence Propagation (MRIP), a novel probabilistic method for heterogeneous networks. They demonstrate that MRIP is useful for predicting links in sparse networks, which present a significant challenge due to the severe disproportion of the number of potential links to the number of real formed links.