k-WL is incomplete on simple spectrum graphs; PRiSM is the first provably complete canonicalization for their eigendecompositions.
Open graph benchmark: Datasets for machine learning on graphs
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
fields
cs.LG 6verdicts
UNVERDICTED 6representative citing papers
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.
S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
DRIFT benchmark shows substantial performance degradation for continual graph learning methods under task-free continuous distribution shifts modeled via Gaussian mixtures.
PRISM iteratively transforms semantic priors into behavior-conditioned posteriors via cross-modal refinement to improve representation learning on dynamic text-attributed graphs.
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
citing papers explorer
-
Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
k-WL is incomplete on simple spectrum graphs; PRiSM is the first provably complete canonicalization for their eigendecompositions.
-
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.
-
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
-
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.
-
PRISM: Iterative Cross-Modal Posterior Refinement for Dynamic Text-Attributed Graphs
PRISM iteratively transforms semantic priors into behavior-conditioned posteriors via cross-modal refinement to improve representation learning on dynamic text-attributed graphs.
-
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.