Characterizes constituents of n-qubit graph quantum ML models and supplies a toolbox enabling integration with classical models, generalization of prior GQML approaches, and classical pre-training.
hub
TUDataset: A collection of benchmark datasets for learning with graphs
34 Pith papers cite this work. Polarity classification is still indexing.
34
Pith papers citing it
abstract
Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDataset for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.
hub tools
citation-role summary
background 2
dataset 2
citation-polarity summary
representative citing papers
Introduces graph-label conditioned (GLC) and embedding-label conditioned (ELC) reconstruction attacks on GNNs that achieve high-quality graph recovery in black-box settings on NCI1, PROTEINS and AIDS datasets using four distributional metrics.
STRAND treats persistence diagrams as survival data to derive a calibrated two-sample test, interpretable effect sizes, and a 1-Wasserstein-stable feature vector from one representation.
Introduces Hypergraph U-Nets with PHPool and PHUnpool operators derived from hierarchical clustering dendrograms for hypergraph reconstruction, classification, and anomaly detection.
GAUGE is a pretrainable Riemannian graph model with neural vector bundles and a Dirichlet loss that captures transferable intrinsic geometry, validated on zero-shot link prediction and graph isomorphism.
AbstainGNN is a framework that jointly models prediction and abstention in GNNs for graph classification, using a PAC-Bayesian-derived unified objective and two-stage training to achieve better accuracy at given rejection rates than prior abstention methods.
Quantum-inspired observables reveal poor signal routing in standard spectral GNNs and motivate Schrödinger GNNs with superior propagation capacity.
GraphIP-Bench is a new unified benchmark showing GNN model extraction succeeds at moderate query budgets while most defenses fail to prevent it or retain verification signals on surrogates.
Higher-order persistence diagrams are defined recursively via interval containments, and their aggregations can be evaluated in nearly linear time using zeta transforms instead of explicit pair enumeration.
CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
Concept Graph Convolutions perform message passing on node concepts to increase interpretability of graph neural networks without losing task performance.
R2G is a multi-view circuit graph benchmark showing that representation choice affects GNN accuracy more than model architecture, with node-centric views and deeper decoders performing best.
HyperX is the first end-to-end FPGA accelerator for Nyström-based HDC graph classification, delivering 6.85× speedup and 169× energy efficiency over CPU baselines plus 3.4% average accuracy gain on TUDataset benchmarks.
Logic-based Weisfeiler-Leman variants enable graph-to-table conversion for classification that matches GNN and graph transformer accuracy while running 5-20x faster without GPUs.
HSG-12M is a large dataset of spatial multigraphs derived from non-Hermitian crystal energy spectra via the Poly2Graph pipeline, positioned as the first large-scale benchmark of this graph type.
RelSC is a new graph regression benchmark from program graphs with execution time labels, released in homogeneous (RelSC-H) and multi-relational (RelSC-M) variants to study representation effects.
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
The paper demonstrates a black-box model extraction attack on graph classification models that leverages binary subgraph explanations to guide Monte Carlo edge sensitivity estimation with concentration guarantees.
A label-free Group Lasso method estimates important subgraphs in pretrained GNNs by incorporating domain structural knowledge.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
GraphNetz supplies an automated statistical pipeline for GNN benchmarking that includes per-cell confidence intervals, paired tests with multiple-comparison correction, and critical-difference diagrams across tasks and datasets.
Subgraph Concept Network is a new GNN architecture that distills meaningful concepts at node, subgraph, and graph levels via soft clustering to improve explainability while maintaining competitive accuracy.
HISTOGRAPH applies unified layer-wise attention followed by node-wise attention over historical GNN activations to improve graph classification, especially in deep models.
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
citing papers explorer
-
Quantum machine learning models for graphs
Characterizes constituents of n-qubit graph quantum ML models and supplies a toolbox enabling integration with classical models, generalization of prior GQML approaches, and classical pre-training.
-
Rethinking Generative Reconstruction Attacks against Graph Neural Network Models
Introduces graph-label conditioned (GLC) and embedding-label conditioned (ELC) reconstruction attacks on GNNs that achieve high-quality graph recovery in black-box settings on NCI1, PROTEINS and AIDS datasets using four distributional metrics.
-
From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features
STRAND treats persistence diagrams as survival data to derive a calibrated two-sample test, interpretable effect sizes, and a 1-Wasserstein-stable feature vector from one representation.
-
Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets
Introduces Hypergraph U-Nets with PHPool and PHUnpool operators derived from hierarchical clustering dendrograms for hypergraph reconstruction, classification, and anomaly detection.
-
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
GAUGE is a pretrainable Riemannian graph model with neural vector bundles and a Dirichlet loss that captures transferable intrinsic geometry, validated on zero-shot link prediction and graph isomorphism.
-
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
AbstainGNN is a framework that jointly models prediction and abstention in GNNs for graph classification, using a PAC-Bayesian-derived unified objective and two-stage training to achieve better accuracy at given rejection rates than prior abstention methods.
-
Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
Quantum-inspired observables reveal poor signal routing in standard spectral GNNs and motivate Schrödinger GNNs with superior propagation capacity.
-
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
GraphIP-Bench is a new unified benchmark showing GNN model extraction succeeds at moderate query budgets while most defenses fail to prevent it or retain verification signals on surrogates.
-
Higher-order Persistence Diagrams
Higher-order persistence diagrams are defined recursively via interval containments, and their aggregations can be evaluated in nearly linear time using zeta transforms instead of explicit pair enumeration.
-
CTQWformer: A CTQW-based Transformer for Graph Classification
CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
-
Concept Graph Convolutions: Message Passing in the Concept Space
Concept Graph Convolutions perform message passing on node concepts to increase interpretability of graph neural networks without losing task performance.
-
Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization
Logic-based Weisfeiler-Leman variants enable graph-to-table conversion for classification that matches GNN and graph transformer accuracy while running 5-20x faster without GPUs.
-
HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals
HSG-12M is a large dataset of spatial multigraphs derived from non-Hermitian crystal energy spectra via the Poly2Graph pipeline, positioned as the first large-scale benchmark of this graph type.
-
A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants
RelSC is a new graph regression benchmark from program graphs with execution time labels, released in homogeneous (RelSC-H) and multi-relational (RelSC-M) variants to study representation effects.
-
Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
-
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
The paper demonstrates a black-box model extraction attack on graph classification models that leverages binary subgraph explanations to guide Monte Carlo edge sensitivity estimation with concentration guarantees.
-
Estimating Subgraph Importance with Structural Prior Domain Knowledge
A label-free Group Lasso method estimates important subgraphs in pretrained GNNs by incorporating domain structural knowledge.
-
Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
-
GraphNetz: Statistical Benchmarking of Graph Neural Networks with Paired Tests and Rank Aggregation
GraphNetz supplies an automated statistical pipeline for GNN benchmarking that includes per-cell confidence intervals, paired tests with multiple-comparison correction, and critical-difference diagrams across tasks and datasets.
-
Subgraph Concept Networks: Concept Levels in Graph Classification
Subgraph Concept Network is a new GNN architecture that distills meaningful concepts at node, subgraph, and graph levels via soft clustering to improve explainability while maintaining competitive accuracy.
-
Learning from Historical Activations in Graph Neural Networks
HISTOGRAPH applies unified layer-wise attention followed by node-wise attention over historical GNN activations to improve graph classification, especially in deep models.
-
Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
-
GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks
GLIP is a joint GNN-LLM pretraining framework that uses augmentation, multi-token selection, a diffusion projector, and combined contrastive plus semantic losses to boost graph classification and reasoning after fine-tuning on limited labels.
-
The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Introduces curvature-stratified evaluation showing relational learning model rankings are stable within curvature regimes but shift across them, making performance geometry-dependent.
-
A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
SPG is a graph foundation model using spectral decomposition via Chebyshev filters and Gromov-Wasserstein prototypes for improved cross-graph transferability.
-
How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Quantum-oriented embeddings deliver consistent gains on structure-driven graph datasets while classical baselines perform adequately on attribute-limited social graphs, under identical training pipelines across five TU datasets and binned QM9.
-
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
GP2F is a dual-branch graph prompting framework that fuses frozen pre-trained knowledge with task-specific adaptation to reduce estimation error and outperform baselines in cross-domain few-shot node and graph classification.
-
OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks
OpenGLT benchmark finds no single GNN architecture dominates graph-level tasks, with subgraph-based models strongest in expressiveness, graph learning and SSL models in robustness, node and pooling models in efficiency, and graph topology partially guiding architecture choice.
-
Ramanujan Graph Rewiring with Non Negative Resistance Curvature
Introduces Ramanujan Propagation as a graph rewiring method for GNNs that leverages Ramanujan graphs to ensure non-negative resistance curvature while preserving local connectivity and outperforming prior rewiring techniques.
-
Fine-Grained Graph Generation through Latent Mixture Scheduling
A novel CVAE with mixture scheduling achieves fine-grained structural control in graph generation, showing high quality and controllability on five datasets.
-
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
A survey compiling graph rewiring techniques for mitigating over-squashing and over-smoothing in GNNs.