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 351400 of 927 papers

TitleStatusHype
Kernel Graph Convolutional Neural NetworksCode0
Kernel method for persistence diagrams via kernel embedding and weight factorCode0
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral EmbeddingCode0
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning ApproachCode0
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural NetworksCode0
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram IntersectionCode0
DAGCN: Dual Attention Graph Convolutional NetworksCode0
IPC: A Benchmark Data Set for Learning with Graph-Structured DataCode0
Curvature-based Pooling within Graph Neural NetworksCode0
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured DataCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter TrendsCode0
Global Weisfeiler-Lehman Graph KernelsCode0
Graph Contrastive Learning with Implicit AugmentationsCode0
Cross-Domain Few-Shot Graph ClassificationCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Improving Subgraph-GNNs via Edge-Level Ego-Network EncodingsCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Counterfactual Explanations for Graph Classification Through the Lenses of DensityCode0
Geometric deep learning on graphs and manifolds using mixture model CNNsCode0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
AutoGEL: An Automated Graph Neural Network with Explicit Link InformationCode0
GraphDIVE: Graph Classification by Mixture of Diverse ExpertsCode0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
SimGNN: A Neural Network Approach to Fast Graph Similarity ComputationCode0
An Experimental Study of the Transferability of Spectral Graph NetworksCode0
ImageNet Classification with Deep Convolutional Neural NetworksCode0
Graph Filtration LearningCode0
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph ClassificationCode0
Graph Fourier Transformer with Structure-Frequency InformationCode0
Graph Fuzzy System: Concepts, Models and AlgorithmsCode0
Density-aware Walks for Coordinated Campaign DetectionCode0
Identity Inference on Blockchain using Graph Neural NetworkCode0
Cooperative Classification and Rationalization for Graph GeneralizationCode0
Generalized Simplicial Attention Neural NetworksCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Generalization Error of Graph Neural Networks in the Mean-field RegimeCode0
A Unified Invariant Learning Framework for Graph ClassificationCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Graph isomorphism UNetCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Improving the interpretability of GNN predictions through conformal-based graph sparsificationCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph ClassificationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelsCode0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
Higher-Order Message Passing for Glycan Representation LearningCode0
How Curvature Enhance the Adaptation Power of Framelet GCNsCode0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On GraphsCode0
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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