Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.
Open graph benchmark: Datasets for machine learning on graphs
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DRIFT benchmark shows substantial performance degradation for continual graph learning methods under task-free continuous distribution shifts modeled via Gaussian mixtures.
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Fused Gromov-Wasserstein Distance with Feature Selection
Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.
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DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
DRIFT benchmark shows substantial performance degradation for continual graph learning methods under task-free continuous distribution shifts modeled via Gaussian mixtures.