Temporal link prediction in multi-relational network

Abstract

Link prediction problem in complex networks has received substantial amount of attention in the field of social network analysis. Though initial studies consider only static snapshot of a network, importance of temporal dimension has been observed and cultivated subsequently. In recent times, multi-domain relationships between node-pairs embedded in real networks have been exploited to boost link prediction performance. In this paper, we combine multi-domain topological features as well as temporal dimension, and propose a robust and efficient feature set called TMLP (Time-aware Multi-relational Link Prediction) for link prediction in dynamic heterogeneous networks. It combines dynamics of graph topology and history of interactions at dyadic level, and exploits time-series model in the feature extraction process. Several experiments on two networks prepared from DBLP bibliographic dataset show that the proposed framework outperforms the existing methods significantly, in predicting future links. It also demonstrates the necessity of combining heterogeneous information with temporal dynamics of graph topology and dyadic history in order to predict future links. Empirical results find that the proposed feature set is robust against longitudinal bias.