Sign prediction in social networks based on tendency rate of equivalent micro-structures

Online social networks have significantly changed the way people shape their everyday communications. Signed networks are a class of social networks in which relations can be positive or negative. These networks emerge in areas where there is interplay between opposite attitudes such as trust and distrust. Recent studies have shown that sign of relationships is predictable using data already present in the network. In this work, we study the sign prediction problem in networks with both positive and negative links and investigate the application of network tendency in the prediction task. Accordingly, we develop a simple algorithm that can infer unknown relation types with high performance. We conduct experiments on three real-world signed networks: Epinions, Slashdot and Wikipedia. Experimental results indicate that the proposed approach outperforms the state of the art methods in terms of both overall accuracy and true negative rate. Furthermore, significantly low computational complexity of the proposed algorithm allows applying it to large-scale datasets.

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