Understanding and Predicting Links in Graphs: A Persistent Homology Perspective

Bhatia, Sumit and Chatterjee, Bapi and Kaul, Manohar and et al, . (2018) Understanding and Predicting Links in Graphs: A Persistent Homology Perspective. arXiv.org.

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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. In this paper, 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 evaluate our approach on seven different real-world datasets and offer directions for future work.

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IITH Creators:
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Item Type: Article
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 19 Dec 2019 08:13
Last Modified: 19 Dec 2019 08:13
URI: http://raiith.iith.ac.in/id/eprint/7194
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