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SNE: Signed Network Embedding

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Advances in Knowledge Discovery and Data Mining (PAKDD 2017)

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Abstract

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.

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Notes

  1. 1.

    http://www.epinions.com/.

  2. 2.

    https://slashdot.org/.

  3. 3.

    https://snap.stanford.edu/data/.

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Acknowledgments

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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Authors and Affiliations

  1. Tongji University, Shanghai, China

    Shuhan Yuan & Yang Xiang

  2. University of Arkansas, Fayetteville, AR, USA

    Xintao Wu

Authors
  1. Shuhan Yuan
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  2. Xintao Wu
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  3. Yang Xiang
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Corresponding author

Correspondence to Yang Xiang .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Yuan, S., Wu, X., Xiang, Y. (2017). SNE: Signed Network Embedding. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-57529-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57528-5

  • Online ISBN: 978-3-319-57529-2

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