---
res:
  bibo_abstract:
  - Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture
    the topological properties of data succinctly at different spatial resolutions.
    For graphical data, the shape, and structure of the neighborhood of individual
    data items (nodes) are 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Sumit
      foaf_name: Bhatia, Sumit
      foaf_surname: Bhatia
  - foaf_Person:
      foaf_givenName: Bapi
      foaf_name: Chatterjee, Bapi
      foaf_surname: Chatterjee
      foaf_workInfoHomepage: http://www.librecat.org/personId=3C41A08A-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-2742-4028
  - foaf_Person:
      foaf_givenName: Deepak
      foaf_name: Nathani, Deepak
      foaf_surname: Nathani
  - foaf_Person:
      foaf_givenName: Manohar
      foaf_name: Kaul, Manohar
      foaf_surname: Kaul
  bibo_doi: 10.1007/978-3-030-36687-2_3
  bibo_volume: 881
  dct_date: 2020^xs_gYear
  dct_identifier:
  - UT:000843927300003
  dct_isPartOf:
  - http://id.crossref.org/issn/1860-949X
  - http://id.crossref.org/issn/1860-9503
  - http://id.crossref.org/issn/9783030366865
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: A persistent homology perspective to the link prediction problem@
...