Link Prediction with Persistent Homology: An Interactive View

Zuoyu Yan · Tengfei Ma · Liangcai Gao · Zhi Tang · Chao Chen

[ Abstract ] [ Livestream: Visit Representation Learning 6 ] [ Paper ]
Thu 22 Jul 7:25 p.m. — 7:30 p.m. PDT

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.

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