Link Prediction in Dynamic Social Networks by Integrating Community Information

In this work, we present a method for link prediction in dynamic network by integrating temporal and community structure in the network. Integration of these approaches carries high rich information for predicting potential links in social networks from different aspects. Experimental results on real datasets show that the integration time model on the two approaches can efficiently predict future links in temporal social networks, and can achieve higher quality results than traditional methods.