Papers with Lorentzian linear feature transformation
Lˆ2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification (2024.lrec-main)
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| Challenge: | Existing linear GCNs perform neural network operations in Euclidean space, which do not capture tree-like hierarchical structure of graphs. |
| Approach: | They propose a Lorentzian linear GCN framework that maps features into hyperbolic space and performs a feature transformation to capture the underlying tree-like structure of data. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy on standard citation networks datasets and 81.3% on PubMed datasets. |