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 1–50 of 927 papers

TitleStatusHype
Density-aware Walks for Coordinated Campaign DetectionCode0
Positional Encoding meets Persistent Homology on GraphsCode0
Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs—0
Improving the Effective Receptive Field of Message-Passing Neural NetworksCode1
Graph Style Transfer for Counterfactual ExplainabilityCode0
Scalable Graph Generative Modeling via Substructure SequencesCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
Schreier-Coset Graph Propagation—0
Rhomboid Tiling for Geometric Graph Deep Learning—0
Efficient Mixed Precision Quantization in Graph Neural NetworksCode0
DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean DatasetsCode0
Graph Fourier Transformer with Structure-Frequency InformationCode0
GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learningCode0
LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisation—0
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured DataCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks—0
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary PerturbationsCode0
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
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter TrendsCode0
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing—0
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
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information BottleneckCode1
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
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet ExcellenceCode2
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation—0
Graph Neural Networks at a Fraction—0
Beyond Message Passing: Neural Graph Pattern MachineCode1
Molecular Fingerprints Are Strong Models for Peptide Function PredictionCode3
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
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing—0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
Cluster-guided Contrastive Class-imbalanced Graph Classification—0
Semi-Implicit Neural Ordinary Differential EquationsCode1
Robustness of Graph Classification: failure modes, causes, and noise-resistant loss in Graph Neural Networks—0
Training MLPs on Graphs without SupervisionCode1
Show:102550
← PrevPage 1 of 19Next →
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