A persistent homology perspective to the link prediction problem SCI Bhatia, Sumit Chatterjee, Bapi ; https://orcid.org/0000-0002-2742-4028 Nathani, Deepak Kaul, Manohar ddc:004 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. Springer Nature 2020 info:eu-repo/semantics/conferenceObject doc-type:ConferenceObject ConferenceObject http://purl.org/coar/resource_type/c_5794 https://research-explorer.ista.ac.at/record/7213 https://research-explorer.ista.ac.at/download/7213/8625 Bhatia S, Chatterjee B, Nathani D, Kaul M. A persistent homology perspective to the link prediction problem. In: <i>Complex Networks and Their Applications VIII</i>. Vol 881. Springer Nature; 2020:27-39. doi:<a href="https://doi.org/10.1007/978-3-030-36687-2_3">10.1007/978-3-030-36687-2_3</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-36687-2_3 info:eu-repo/semantics/altIdentifier/issn/1860-949X info:eu-repo/semantics/altIdentifier/e-issn/1860-9503 info:eu-repo/semantics/altIdentifier/isbn/9783030366865 info:eu-repo/semantics/altIdentifier/wos/000843927300003 info:eu-repo/semantics/openAccess