A Persistent Homology Perspective to the Link Prediction Problem

  • Sumit BhatiaEmail author
  • Bapi Chatterjee
  • Deepak Nathani
  • Manohar Kaul
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)


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.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sumit Bhatia
    • 1
    Email author
  • Bapi Chatterjee
    • 2
  • Deepak Nathani
    • 3
  • Manohar Kaul
    • 3
  1. 1.IBM Research AINew DelhiIndia
  2. 2.Institute of Science and TechnologyKlosterneuburgAustria
  3. 3.Indian Institute of Technology, HyderabadSangareddyIndia

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