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.
Jan 1, 2020
Nov 26, 2019 · 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 ...
Abstract. Persistent homology is a powerful tool in Topological Data Analysis. (TDA) to capture topological properties of data succinctly at different ...
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 ...
Bhatia, S., Chatterjee, B., Nathani, D., Kaul, M.: Understanding and predicting links in graphs: a persistent homology perspective. · Chung, M.K., Bubenik, P., ...
Nov 9, 2018 · In this paper, we propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to ...
Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture topological properties of data succinctly at different spatial resolutions.
In this paper, we propose a novel topological approach to characterize in- teractions between two nodes. Our topological feature, based on the extended ...
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A novel topological approach to characterize interactions between two nodes, based on the extended persistent homology, is proposed and a graph neural ...
We use the pairwise topological feature to enhance the latent representation of a graph neural network and achieve state-of-the-art link prediction results on.