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 251–300 of 927 papers

TitleStatusHype
Network Embedding Exploration Tool (NEExT)Code0
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks—0
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input UtilizationCode0
Structural Entropy Guided Unsupervised Graph Out-Of-Distribution DetectionCode0
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing—0
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter TrendsCode0
Neural Network Graph Similarity Computation Based on Graph FusionCode0
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning—0
Graph Self-Supervised Learning with Learnable Structural and Positional EncodingsCode0
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
Classification of Temporal Graphs using Persistent HomologyCode0
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation—0
Graph Neural Networks at a Fraction—0
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning ApproachCode0
A Unified Invariant Learning Framework for Graph ClassificationCode0
Catch Causal Signals from Edges for Label Imbalance in Graph ClassificationCode0
Weakly Supervised Learning on Large Graphs—0
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements—0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing—0
Cluster-guided Contrastive Class-imbalanced Graph Classification—0
Robustness of Graph Classification: failure modes, causes, and noise-resistant loss in Graph Neural Networks—0
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
From Graph Diffusion to Graph Classification—0
Conditional Distribution Learning on GraphsCode0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
The GECo algorithm for Graph Neural Networks ExplanationCode0
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
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning—0
Tensor-Fused Multi-View Graph Contrastive LearningCode0
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks—0
Conditional Prediction ROC Bands for Graph ClassificationCode0
FIT-GNN: Faster Inference Time for GNNs Using CoarseningCode0
Molecular Topological Profile (MOLTOP) - Simple and Strong Baseline for Molecular Graph ClassificationCode0
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
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
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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