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

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Showing 401–450 of 927 papers

TitleStatusHype
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral EmbeddingCode0
Cooperative Classification and Rationalization for Graph GeneralizationCode0
Generalized Simplicial Attention Neural NetworksCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input UtilizationCode0
Generalization Error of Graph Neural Networks in the Mean-field RegimeCode0
A Unified Invariant Learning Framework for Graph ClassificationCode0
Kernel Graph Convolutional Neural NetworksCode0
A Novel Higher-order Weisfeiler-Lehman Graph ConvolutionCode0
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNsCode0
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter TrendsCode0
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean DatasetsCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Graph Neural Networks Use Graphs When They Shouldn'tCode0
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural NetworksCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
IPC: A Benchmark Data Set for Learning with Graph-Structured DataCode0
On the approximation capability of GNNs in node classification/regression tasksCode0
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph KernelsCode0
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelsCode0
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR ImagesCode0
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured DataCode0
Incorporating Heterophily into Graph Neural Networks for Graph ClassificationCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Improving Subgraph-GNNs via Edge-Level Ego-Network EncodingsCode0
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention MechanismCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
FoSR: First-order spectral rewiring for addressing oversquashing in GNNsCode0
Attention Models in Graphs: A SurveyCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Improving the interpretability of GNN predictions through conformal-based graph sparsificationCode0
Learning Parametrised Graph Shift OperatorsCode0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
Conditional Prediction ROC Bands for Graph ClassificationCode0
Graph Self-Supervised Learning with Learnable Structural and Positional EncodingsCode0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On GraphsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Filtration Surfaces for Dynamic Graph ClassificationCode0
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on GraphsCode0
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph CoarseningCode0
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural NetworksCode0
Generalized Equivariance and Preferential Labeling for GNN Node ClassificationCode0
Fast Tree-Field Integrators: From Low Displacement Rank to Topological TransformersCode0
How Curvature Enhance the Adaptation Power of Framelet GCNsCode0
Community Detection with Graph Neural NetworksCode0
FIT-GNN: Faster Inference Time for GNNs Using CoarseningCode0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
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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
8δ-2-LWLAccuracy85.5—Unverified
9DUGNNAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified