Abstract
Network embedding is an important pre-process for analysing large scale information networks. Several network embedding algorithms have been proposed for unsigned social networks. However, these methods cannot be simply migrate to signed social networks which have both positive and negative relationships. In this paper, we present our signed social network embedding model which is based on the word embedding model. To deal with two kinds of links, we define two relationships: neighbour relationship and common neighbour relationship, as well as design a bias random walk procedure. In order to further improve interpretation of the representation vectors, the follow-proximally-regularized-leader online learning algorithm is introduced to the traditional word embedding framework to acquire sparse representations. Extensive experiments were carried out to compare our algorithm with three state-of-the-art methods for community detection and sign prediction tasks. The experimental results demonstrate that our algorithm performs better than the comparison algorithms on most signed social networks.
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Acknowledgements
This work is partly funded by the National Nature Science Foundation of China (nos. 61672329, 61373149, 61472233, 61572300, and 81273704), Shandong Provincial Project for Science and Technology Development (no. 2014GGX101026), Shandong Provincial Project of Education Scientific Plan (no. ZK1437B010), Taishan Scholar Program of Shandong Province (nos. TSHW201502038 and 20110819), and Shandong Provincial Project of Exquisite Course (nos. 2012BK294, 2013BK399, and 2013BK402).
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Hu, B., Wang, H., Yu, X. et al. Sparse network embedding for community detection and sign prediction in signed social networks. J Ambient Intell Human Comput 10, 175–186 (2019). https://doi.org/10.1007/s12652-017-0630-1
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DOI: https://doi.org/10.1007/s12652-017-0630-1