Method · Kernel and closed-form inner solvers
KIDD
Kernel Ridge Regression-Based Graph Dataset Distillation
Zhe Xu, Yuzhong Chen, Menghai Pan
KDD 2023 · first public 2023-01
In one paragraph
KIDD (Kernel ridge regression-based graph Dataset Distillation) replaces the inner GNN training loop with kernel ridge regression, using a purpose-built graph kernel (LiteGNTK, related to the random-walk graph kernel) so the condensation objective has a closed-form solution. The paper reports strong empirical performance across seven real-world graph datasets, in some cases with condensed graphs outperforming the full training set using as few as 1.65% of the original training graphs.
Where it sits
- Kernel and closed-form inner solvers (Bi-level performance matching)
- Setting: Graphs
BibTeX (generated; prefer the venue's official entry)
@article{xu2023kernel,
title = {Kernel Ridge Regression-Based Graph Dataset Distillation},
author = {Zhe Xu and Yuzhong Chen and Menghai Pan},
journal = {KDD 2023},
year = {2023}
}Nearby in Kernel and closed-form inner solvers
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