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mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

Yu Shi,Fangqiu Han,Xinwei He,Xinran He,Carl Yang,Jie Luo,Jiawei Han

Published 2018 in arXiv.org

ABSTRACT

Multi-view networks are ubiquitous in real-world applications. In order to extract knowledge or business value, it is of interest to transform such networks into representations that are easily machine-actionable. Meanwhile, network embedding has emerged as an effective approach to generate distributed network representations. Therefore, we are motivated to study the problem of multi-view network embedding, with a focus on the characteristics that are specific and important in embedding this type of networks. In our practice of embedding real-world multi-view networks, we identify two such characteristics, which we refer to as preservation and collaboration. We then explore the feasibility of achieving better embedding quality by simultaneously modeling preservation and collaboration, and propose the mvn2vec algorithms. With experiments on a series of synthetic datasets, an internal Snapchat dataset, and two public datasets, we further confirm the presence and importance of preservation and collaboration. These experiments also demonstrate that better embedding can be obtained by simultaneously modeling the two characteristics, while not over-complicating the model or requiring additional supervision.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    arXiv.org

  • Publication date

    2018-01-19

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1801.06597
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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