Publication
ICML 2021
Conference paper

Persistence Homology for Link Prediction: An Interactive View

ICML 2021
Download paper

Abstract

Link prediction is an important learning task for graph-structured data. In this paper, we propose a novel topological approach to characterize interactions between two nodes. Our topological feature, based on the extended persistence homology, encodes rich structural information regarding the multi-hop paths connecting nodes. Based on this feature, we propose a graph neural network method that outperforms state-of-the-arts on different benchmarks. As another contribution, we propose a novel algorithm to more efficiently compute the extended persistent diagrams for graphs. This algorithm can be generally applied to accelerate many other topological methods for graph learning tasks.

Date

18 Jul 2021

Publication

ICML 2021

Authors

Tags

Resources

Share