SOTAVerified

Graph Classification

Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

( Image credit: Hierarchical Graph Pooling with Structure Learning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 927 papers

TitleStatusHype
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Gradient Inversion Attack on Graph Neural Networks—0
MADE: Graph Backdoor Defense with Masked Unlearning—0
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
From Graph Diffusion to Graph Classification—0
Conditional Distribution Learning on GraphsCode0
The GECo algorithm for Graph Neural Networks ExplanationCode0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network—0
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation—0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks—0
Towards Dynamic Message Passing on GraphsCode0
RAGraph: A General Retrieval-Augmented Graph Learning FrameworkCode1
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning—0
Conditional Prediction ROC Bands for Graph ClassificationCode0
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks—0
Tensor-Fused Multi-View Graph Contrastive LearningCode0
FIT-GNN: Faster Inference Time for GNNs Using CoarseningCode0
Molecular Topological Profile (MOLTOP) - Simple and Strong Baseline for Molecular Graph ClassificationCode0
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification—0
Degree-Conscious Spiking Graph for Cross-Domain Adaptation—0
Vector-ICL: In-context Learning with Continuous Vector RepresentationsCode1
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph—0
Higher-Order Message Passing for Glycan Representation LearningCode0
PropEnc: A Property Encoder for Graph Neural Networks—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique GraphsCode0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
GSpect: Spectral Filtering for Cross-Scale Graph Classification—0
GSTAM: Efficient Graph Distillation with Structural Attention-MatchingCode0
Learning Tree-Structured Composition of Data AugmentationCode0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
Graph Classification with GNNs: Optimisation, Representation and Inductive BiasCode0
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed GraphsCode0
Graph Triple Attention Network: A Decoupled PerspectiveCode0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Knowledge Probing for Graph Representation Learning—0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
AutoRDF2GML: Facilitating RDF Integration in Graph Machine LearningCode1
Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques—0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—0
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation LearningCode0
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All datasetsPROTEINSMUTAGNCI1D&DENZYMESIMDb-BCOLLABPTCIMDb-MNCI109CIFAR10 100kREDDIT-B

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762—Unverified
2HGP-SLAccuracy84.91—Unverified
3rLap (unsupervised)Accuracy84.3—Unverified
4TFGW ADJ (L=2)Accuracy82.9—Unverified
5FIT-GNNAccuracy82.1—Unverified
6DUGNNAccuracy81.7—Unverified
7MEWISPoolAccuracy80.71—Unverified
8CIN++Accuracy80.5—Unverified
9MAGPoolAccuracy80.36—Unverified
10SAEPoolAccuracy80.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100—Unverified
2MEWISPoolAccuracy96.66—Unverified
3TFGW ADJ (L=2)Accuracy96.4—Unverified
4GIUNetAccuracy95.7—Unverified
5G_InceptionAccuracy95—Unverified
6GICAccuracy94.44—Unverified
7CIN++Accuracy94.4—Unverified
8sGINAccuracy94.14—Unverified
9CANAccuracy94.1—Unverified
10Deep WL SGN(0,1,2)Accuracy93.68—Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1—Unverified
2WKPI-kmeansAccuracy87.2—Unverified
3FGW wl h=4 spAccuracy86.42—Unverified
4WL-OA KernelAccuracy86.1—Unverified
5WL-OAAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified