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Link Prediction for Directed Graphs

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Social Network-Based Recommender Systems

Abstract

In this chapter we introduce link prediction methods and metrics for directed graphs. We compare well known similarity metrics and their suitability for link prediction in directed social networks. We advance existing techniques and propose mining of subgraph patterns that are used to predict links in networks such as GitHub, GooglePlus, and Twitter. Our results show that the proposed metrics and techniques yield more accurate predictions when compared with metrics not accounting for the directed nature of the underlying networks.

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Notes

  1. 1.

    The upper bound for which the Data Layer has been tested was a network consisting of approximately 5 × 107 nodes and 1. 5 × 109 edges.

  2. 2.

    Web page: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm.

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Authors and Affiliations

  1. Siemens Corporate Technology, Wien, Austria

    Daniel Schall

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  1. Daniel Schall
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Schall, D. (2015). Link Prediction for Directed Graphs. In: Social Network-Based Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-22735-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-22735-1_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22734-4

  • Online ISBN: 978-3-319-22735-1

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