A Link Prediction Approach in Temporal Networks Based on Game Theory

Link prediction is an important task in complex network analysis, which can be applied to many real-world practical scenarios such as recommender systems, information retrieval, and marketing analysis. Different from the traditional link prediction problem, this paper predicts the existence of the link at any time in the future based on the set of temporal links in a given time window, that is, the evolution mechanism of the temporal network. To explore this question, we propose a novel semi-supervised learning framework, which integrates both survival analysis and game theory. First, we carefully define the ε-adjacent network sequence, and make use of time stamp on each link to generate the ground-truth network evolution sequence. Next, to capture the law of network evolution, we employ the Cox proportional hazard model to study the relative hazard associated with each temporal link, so as to estimate the covariate’s coefficient associated with a set of neighborhood-based proximity features. To compress the searching space, we further propose a game theory based two-way selection mechanism to inference the future network topology. We finally propose a network evolution prediction algorithm based on autonomy-oriented computing, and demonstrate both the effectiveness and the efficiency of the proposed algorithm on real-world temporal networks.