SNE: Signed Network Embedding

  • Shuhan Yuan
  • Xintao Wu
  • Yang XiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)


Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.



The authors acknowledge the support from the National Natural Science Foundation of China (71571136), the 973 Program of China (2014CB340404), and the Research Project of Science and Technology Commission of Shanghai Municipality (16JC1403000, 14511108002) to Shuhan Yuan and Yang Xiang, and from National Science Foundation (1564250) to Xintao Wu. This research was conducted while Shuhan Yuan visited University of Arkansas. Yang Xiang is the corresponding author of the paper.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Tongji UniversityShanghaiChina
  2. 2.University of ArkansasFayettevilleUSA

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