A node representation learning approach for link prediction in social networks using game theory and K-core decomposition
Azarbaijan Shahid Madani University,
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Received in final form: 6 August 2019
Published online: 8 October 2019
The role of social networks in people’s daily life is undeniable. Link prediction is one of the most important tasks of complex network analysis. Predicting links is currently becoming a concerned topic in social network analysis. Although many link prediction methods have been proposed in the recent years, most of the existing link prediction methods have unsatisfactory performance because of high time complexity, network size, sparsity, and similarity measures between node pairs for processing topological information. In this paper, we proposed a method for node representation learning and also a new embedding technique is used for generating latent features. We also proposed an improved version of the weighted random walk based on game theoretical technique and k-core decomposition. Node representations are generated via skipgram method. Although most of the link prediction methods have high time complexity, since our method uses Stochastic Gradient Descent for the optimization process, it has linear time complexity with respect to the number of vertices. This causes our algorithm to be scalable to large networks. In addition to that, sparsity is a huge challenge in complex networks and we cannot infer enough information from the structure of the network to make predictions. By learning a low-dimensional representation that captures the network structure, classification of nodes and edges can be done more easily. The performance of the proposed method was evaluated with some benchmark heuristic scores and state-of-the-art techniques on link prediction in several real-world networks. The experimental results show that the proposed method obtains higher accuracy in comparison with considered methods and measures. However, the time complexity is not improved effectively.
Key words: Statistical and Nonlinear Physics
© EDP Sciences / Società Italiana di Fisica / Springer-Verlag GmbH Germany, part of Springer Nature, 2019