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One of the major issues in signed networks is to use network structure topredict the missing sign of an edge. In this paper, we introduce a novelprobabilistic approach for the sign prediction problem. The main characteristicof the proposed models is their ability to adapt to the sparsity level of aninput network. The sparsity of networks is one of the major reasons for thepoor performance of many link prediction algorithms, in general, and signprediction algorithms, in particular. Building a model that has an ability toadapt to the sparsity of the data has not yet been considered in the previousrelated works. We suggest that there exists a dilemma between local and globalstructures and attempt to build sparsity adaptive models by resolving thisdilemma. To this end, we propose probabilistic prediction models based on localand global structures and integrate them based on the concept of smoothing. Themodel relies more on the global structures when the sparsity increases, whereasit gives more weights to the information obtained from local structures for lowlevels of the sparsity. The proposed model is assessed on three real-worldsigned networks, and the experiments reveal its consistent superiority over thestate of the art methods. As compared to the previous methods, the proposedmodel not only better handles the sparsity problem, but also has lowercomputational complexity and can be updated using real-time data streams.

Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information