Link Prediction with Persistent Homology: An Interactive View
This work addresses link prediction for graph-structured data, offering a novel topological approach that improves performance and includes an efficient algorithm for broader applications in graph learning.
The authors tackled link prediction in graphs by introducing a topological feature based on extended persistent homology to capture multi-hop path information, and their graph neural network method outperformed state-of-the-art models on various benchmarks.
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.