Quantum-inspired observables reveal poor signal routing in standard spectral GNNs and motivate Schrödinger GNNs with superior propagation capacity.
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TUDataset: A collection of benchmark datasets for learning with graphs
22 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
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 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 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.
The paper claims current graph condensation approaches are flawed due to full-dataset training requirements, high overhead, poor generalization, and misleading evaluation metrics, calling for a reset toward lightweight and architecture-agnostic methods.
A novel CVAE with mixture scheduling achieves fine-grained structural control in graph generation, showing high quality and controllability on five datasets.
A survey compiling graph rewiring techniques for mitigating over-squashing and over-smoothing in GNNs.
citing papers explorer
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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.
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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.
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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.
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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.
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R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII
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.
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Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
The paper claims current graph condensation approaches are flawed due to full-dataset training requirements, high overhead, poor generalization, and misleading evaluation metrics, calling for a reset toward lightweight and architecture-agnostic methods.
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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.
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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.
- GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?