Joint multi-label learning and feature extraction for temporal link prediction

Networks derived from various disciplinary of sociality and nature are dynamic and incomplete, and temporal link prediction has wide applications in recommendation system and data mining system, etc. The current algorithms first obtain features by exploiting the topological or latent structure of networks, and then predict temporal links based on the obtained features. These algorithms are criticized by the separation of feature extraction and link prediction, which fails to fully characterize the dynamics of networks, resulting in undesirable performance. To overcome this problem, we propose a novel algorithm by joint multi-label learning and feature extraction (called MLjFE), where temporal link prediction and feature extraction are integrated into an overall objective function. The main advantage of MLjFE is that the features and parameter matrix for temporal link prediction are simultaneously learned during optimization procedure, which is more precise to capture dynamics of networks, improving the performance of algorithms. The experimental results on a number of artificial and real-world temporal networks demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods, showing joint learning with feature extraction and temporal link prediction is promising.