Persistence Homology for Link Prediction: An Interactive View

• URL: http://arxiv.org/abs/2102.10255v1
• Date: Sat, 20 Feb 2021 04:33:59 GMT
• Title: Persistence Homology for Link Prediction: An Interactive View
• Authors: Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
• Abstract summary: Link prediction is an important learning task for graph-structured data. We propose a novel topological approach to characterize interactions between two nodes. We also propose a graph neural network method that outperforms state-of-the-arts on different benchmarks.
• Score: 15.068319518015421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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