Link Prediction with Persistent Homology: An Interactive View
Machine Learning
2021-06-15 v2
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 persistent 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 persistence diagrams for graphs. This algorithm can be generally applied to accelerate many other topological methods for graph learning tasks.
Cite
@article{arxiv.2102.10255,
title = {Link Prediction with Persistent Homology: An Interactive View},
author = {Zuoyu Yan and Tengfei Ma and Liangcai Gao and Zhi Tang and Chao Chen},
journal= {arXiv preprint arXiv:2102.10255},
year = {2021}
}
Comments
Accepted in ICML2021