Partial random data augmentation matches full group augmentation's minimax rates up to vanishing approximation error for classical learning problems, but exact invariance requires the full group for expressive hypotheses.
Graph data augmentation for graph machine learning: A survey
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
Introduces the SORB benchmark showing that sparsification and coarsening effects on influence maximization performance depend strongly on network type and evaluation metric.
A target-aware solver-free data generation pipeline plus an LPGNN that uses linear-programming residuals produces fast, correctly labeled training data and improves GNN-based SAT prediction.
AEGIS is an edge resampling framework that enhances link prediction in edge-sparse bipartite graphs, showing benefits from semantic augmentation on text-rich data.
An information-theoretic framework generates novel graphs via latent mixture modeling with novelty and reliability enforced through minimum description length, backed by convergence guarantees and experiments on graph datasets.
citing papers explorer
-
Data Augmentation: A Fourier Analysis Perspective
Partial random data augmentation matches full group augmentation's minimax rates up to vanishing approximation error for classical learning problems, but exact invariance requires the full group for expressive hypotheses.
-
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.
-
Target-Aware Data Augmentation for SAT Prediction
A target-aware solver-free data generation pipeline plus an LPGNN that uses linear-programming residuals produces fast, correctly labeled training data and improves GNN-based SAT prediction.
-
AEGIS: Authentic Edge Growth In Sparsity for Link Prediction in Edge-Sparse Bipartite Knowledge Graphs
AEGIS is an edge resampling framework that enhances link prediction in edge-sparse bipartite graphs, showing benefits from semantic augmentation on text-rich data.
-
An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modeling
An information-theoretic framework generates novel graphs via latent mixture modeling with novelty and reliability enforced through minimum description length, backed by convergence guarantees and experiments on graph datasets.