Introduces the SORB benchmark showing that sparsification and coarsening effects on influence maximization performance depend strongly on network type and evaluation metric.
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
M2D distillation augments input graphs with model-derived features and structure, letting simple student GNNs match teacher performance while exposing mechanisms such as attention and fairness directly in the data.
Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.
citing papers explorer
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Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark
Introduces the SORB benchmark showing that sparsification and coarsening effects on influence maximization performance depend strongly on network type and evaluation metric.
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From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning
M2D distillation augments input graphs with model-derived features and structure, letting simple student GNNs match teacher performance while exposing mechanisms such as attention and fairness directly in the data.
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Rapid training of Hamiltonian graph networks using random features
Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.