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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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. 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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