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 701750 of 927 papers

TitleStatusHype
Positional Encoding meets Persistent Homology on GraphsCode0
Structure-Aware Robustness Certificates for Graph ClassificationCode0
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation LearningCode0
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary PerturbationsCode0
Density-aware Walks for Coordinated Campaign DetectionCode0
Propagation kernels: efficient graph kernels from propagated informationCode0
Provably Powerful Graph NetworksCode0
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph ClassificationCode0
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input UtilizationCode0
Quadratic GCN for Graph ClassificationCode0
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
A Persistent Weisfeiler–Lehman Procedure for Graph ClassificationCode0
Harnessing the Power of Large Language Model for Uncertainty Aware Graph ProcessingCode0
HATS: A Hierarchical Graph Attention Network for Stock Movement PredictionCode0
DEGREE: Decomposition Based Explanation For Graph Neural NetworksCode0
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph CoarseningCode0
Learning Deep Graph Representations via Convolutional Neural NetworksCode0
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical GuaranteesCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Hard Label Black Box Node Injection Attack on Graph Neural NetworksCode0
Randomized Schur Complement Views for Graph Contrastive LearningCode0
Haar Graph PoolingCode0
GSTAM: Efficient Graph Distillation with Structural Attention-MatchingCode0
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation PoolingCode0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
Higher-Order Message Passing for Glycan Representation LearningCode0
On the approximation capability of GNNs in node classification/regression tasksCode0
Graph U-NetsCode0
Graph Triple Attention Network: A Decoupled PerspectiveCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
How Curvature Enhance the Adaptation Power of Framelet GCNsCode0
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean DatasetsCode0
Graph Style Transfer for Counterfactual ExplainabilityCode0
Graph Star Net for Generalized Multi-Task LearningCode0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On GraphsCode0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
Debiasing Graph Neural Networks via Learning Disentangled Causal SubstructureCode0
Graph Self-Supervised Learning with Learnable Structural and Positional EncodingsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Identity Inference on Blockchain using Graph Neural NetworkCode0
AutoGEL: An Automated Graph Neural Network with Explicit Link InformationCode0
DDGK: Learning Graph Representations for Deep Divergence Graph KernelsCode0
ImageNet Classification with Deep Convolutional Neural NetworksCode0
Generalizing Downsampling from Regular Data to GraphsCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Graph Perceiver IO: A General Architecture for Graph Structured DataCode0
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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