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

Showing 801850 of 927 papers

TitleStatusHype
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
An Experimental Study of the Transferability of Spectral Graph NetworksCode0
The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph ClassificationCode0
A Unified Invariant Learning Framework for Graph ClassificationCode0
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph ClassificationCode0
Learning Convolutional Neural Networks for GraphsCode0
Graph Fuzzy System: Concepts, Models and AlgorithmsCode0
TopoGCL: Topological Graph Contrastive LearningCode0
Learning Graph-Level Representations with Recurrent Neural NetworksCode0
Topological Pooling on GraphsCode0
Graph Fourier Transformer with Structure-Frequency InformationCode0
Scalable Graph Generative Modeling via Substructure SequencesCode0
Graph Filtration LearningCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Learning Parametrised Graph Shift OperatorsCode0
SimGNN: A Neural Network Approach to Fast Graph Similarity ComputationCode0
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernelsCode0
Learning the mechanisms of network growthCode0
An End-to-End Deep Learning Architecture for Graph ClassificationCode0
Learning Tree-Structured Composition of Data AugmentationCode0
Learning Universal Adversarial Perturbations with Generative ModelsCode0
Analysis and Approximate Inference of Large Random Kronecker GraphsCode0
Search to Capture Long-range Dependency with Stacking GNNs for Graph ClassificationCode0
GraphDIVE: Graph Classification by Mixture of Diverse ExpertsCode0
Second-Order Global Attention Networks for Graph Classification and RegressionCode0
LightGCN: Evaluated and EnhancedCode0
Graph Convolutional Networks with EigenPoolingCode0
Graph Contrastive Learning with Implicit AugmentationsCode0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
Curvature-based Pooling within Graph Neural NetworksCode0
Graph Classification with GNNs: Optimisation, Representation and Inductive BiasCode0
Graph Classification using Structural AttentionCode0
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive LearningCode0
Cross-Domain Few-Shot Graph ClassificationCode0
Graph Capsule Convolutional Neural NetworksCode0
GraphAttacker: A General Multi-Task GraphAttack FrameworkCode0
Self-Attention Graph PoolingCode0
GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learningCode0
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR ImagesCode0
GraKeL: A Graph Kernel Library in PythonCode0
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram IntersectionCode0
Conditional Distribution Learning on GraphsCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Wasserstein Graph Distance Based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman SubtreesCode0
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention MechanismCode0
Global Weisfeiler-Lehman Graph KernelsCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Counterfactual Explanations for Graph Classification Through the Lenses of DensityCode0
Geometric deep learning on graphs and manifolds using mixture model CNNsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762Unverified
2HGP-SLAccuracy84.91Unverified
3rLap (unsupervised)Accuracy84.3Unverified
4TFGW ADJ (L=2)Accuracy82.9Unverified
5FIT-GNNAccuracy82.1Unverified
6DUGNNAccuracy81.7Unverified
7MEWISPoolAccuracy80.71Unverified
8CIN++Accuracy80.5Unverified
9MAGPoolAccuracy80.36Unverified
10SAEPoolAccuracy80.36Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100Unverified
2MEWISPoolAccuracy96.66Unverified
3TFGW ADJ (L=2)Accuracy96.4Unverified
4GIUNetAccuracy95.7Unverified
5G_InceptionAccuracy95Unverified
6GICAccuracy94.44Unverified
7CIN++Accuracy94.4Unverified
8sGINAccuracy94.14Unverified
9CANAccuracy94.1Unverified
10Deep WL SGN(0,1,2)Accuracy93.68Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1Unverified
2WKPI-kmeansAccuracy87.2Unverified
3FGW wl h=4 spAccuracy86.42Unverified
4WL-OA KernelAccuracy86.1Unverified
5WL-OAAccuracy86.1Unverified
6FGW wl h=2 spAccuracy85.82Unverified
7WWLAccuracy85.75Unverified
8DUGNNAccuracy85.5Unverified
9δ-2-LWLAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified