A Persistent Homology Perspective to the Link Prediction Problem

Bhatia, Sumit and Chatterjee, Bapi and Kaul, Manohar and et al, . (2020) A Persistent Homology Perspective to the Link Prediction Problem. In: International Conference on Complex Networks and Their Applications, 10-12 December 2019, Lisbon, Portugal.

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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.

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IITH Creators:	IITH Creators ORCiD Kaul, Manohar UNSPECIFIED
Item Type:	Conference or Workshop Item (Paper)
Subjects:	Computer science
Divisions:	Department of Computer Science & Engineering
Depositing User:	Team Library
Date Deposited:	19 Dec 2019 09:22
Last Modified:	22 Oct 2022 09:44
URI:	http://raiithold.iith.ac.in/id/eprint/7195
Publisher URL:	http://doi.org/10.1007/978-3-030-36687-2_3
Related URLs:	Google Scholar

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