Adaptive Multiple Non-negative Matrix Factorization for Temporal Link Prediction in Dynamic Networks

The prediction of mobility, topology and traffic is an effective technique to improve the performance of various network systems, which can be generally represented as the temporal link prediction problem. In this paper, we propose a novel adaptive multiple non-negative matrix factorization (AM-NMF) method from the view of network embedding to cope with such problem. Under the framework of non-negative matrix factorization (NMF), the proposed method embeds the dynamic network into a low-dimensional hidden space, where the characteristics of different network snapshots are comprehensively preserved. Especially, our new method can effectively incorporate the hidden information of different time slices, because we introduce a novel adaptive parameter to automatically adjust the relative contribution of different terms in the uniform model. Accordingly, the prediction result of future network topology can be generated by conducting the inverse process of NMF form the shared hidden space. Moreover, we also derive the corresponding solving strategy whose convergence can be ensured. As an illustration, the new model will be applied to various network datasets such as human mobility networks, vehicle mobility networks, wireless mesh networks and data center networks. Experimental results show that our method outperforms some other state-of-the-art methods for the temporal link prediction of both unweighted and weighted networks.