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 501–550 of 927 papers

TitleStatusHype
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification—0
Graph Contrastive Learning with Implicit AugmentationsCode0
Application of Graph Neural Networks and graph descriptors for graph classification—0
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification—0
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations—0
Improving Graph Neural Networks with Learnable Propagation Operators—0
Graph Fuzzy System: Concepts, Models and AlgorithmsCode0
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex ClusteringCode0
Beyond Homophily with Graph Echo State Networks—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
Efficient Automatic Machine Learning via Design GraphsCode0
FoSR: First-order spectral rewiring for addressing oversquashing in GNNsCode0
Test-Time Training for Graph Neural Networks—0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Pooling Strategies for Simplicial Convolutional NetworksCode0
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language ProcessingCode0
Graph Classification via Discriminative Edge Feature Learning—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Debiasing Graph Neural Networks via Learning Disentangled Causal SubstructureCode0
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks—0
Joint Reconstruction and Parcellation of Cortical Surfaces—0
Cell Attention NetworksCode0
Explainability in subgraphs-enhanced Graph Neural NetworksCode0
SPGP: Structure Prototype Guided Graph Pooling—0
Graph Contrastive Learning with Cross-view Reconstruction—0
Graph Perceiver IO: A General Architecture for Graph Structured DataCode0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability—0
Reinforced Continual Learning for GraphsCode0
A Class-Aware Representation Refinement Framework for Graph Classification—0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph ClassificationCode0
Second-Order Global Attention Networks for Graph Classification and RegressionCode0
Learnable Filters for Geometric Scattering Modules—0
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification—0
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference—0
Generalizing Downsampling from Regular Data to GraphsCode0
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations—0
Maximal Independent Vertex Set applied to Graph Pooling—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
Wasserstein Graph Distance Based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman SubtreesCode0
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training—0
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Semi-Supervised Hierarchical Graph Classification—0
Fundamental Limits in Formal Verification of Message-Passing Neural Networks—0
A Simple yet Effective Method for Graph ClassificationCode0
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph ClassificationCode0
Template based Graph Neural Network with Optimal Transport DistancesCode0
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity LearningCode0
Asynchronous Neural Networks for Learning in Graphs—0
Show:102550
← PrevPage 11 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