FRINK Linked Data Fragments server

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• W3130079480 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." @default.
• W3130079480 created "2021-03-01" @default.
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• W3130079480 date "2021-02-20" @default.
• W3130079480 modified "2023-09-25" @default.
• W3130079480 title "Persistence Homology for Link Prediction: An Interactive View." @default.
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