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International Conference on Complex Networks and Their Applications

COMPLEX NETWORKS 2019: Complex Networks and Their Applications VIII pp 27–39Cite as

A Persistent Homology Perspective to the Link Prediction Problem

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

Abstract

Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture topological properties of data succinctly at different spatial resolutions. For graphical data, shape and structure of the neighborhood of individual data items (nodes) is an essential means of characterizing their properties. We propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to address the problem of link prediction. We achieve encouraging results on nine different real-world datasets that attest to the potential of persistent homology based methods for network analysis.

B. Chatterjee—Supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 754411 (ISTPlus).

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Notes

  1. 1.

    https://topology.ima.umn.edu/node/53.

  2. 2.

    https://bitbucket.org/grey_narn/hera/src/master/.

  3. 3.

    https://github.com/sumit-research/persistent-homology-link-prediction.

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Author information

Authors and Affiliations

  1. IBM Research AI, New Delhi, India

    Sumit Bhatia

  2. Institute of Science and Technology, Klosterneuburg, Austria

    Bapi Chatterjee

  3. Indian Institute of Technology, Hyderabad, Sangareddy, India

    Deepak Nathani & Manohar Kaul

Authors
  1. Sumit Bhatia
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  2. Bapi Chatterjee
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  3. Deepak Nathani
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  4. Manohar Kaul
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Corresponding author

Correspondence to Sumit Bhatia .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Bhatia, S., Chatterjee, B., Nathani, D., Kaul, M. (2020). A Persistent Homology Perspective to the Link Prediction Problem. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_3

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