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 851900 of 927 papers

TitleStatusHype
Generalized Simplicial Attention Neural NetworksCode0
Cooperative Classification and Rationalization for Graph GeneralizationCode0
Semi-Supervised Graph Classification: A Hierarchical Graph PerspectiveCode0
Mirage: Model-Agnostic Graph Distillation for Graph ClassificationCode0
Generalization Error of Graph Neural Networks in the Mean-field RegimeCode0
Wasserstein Weisfeiler-Lehman Graph KernelsCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Modeling Edge Features with Deep Bayesian Graph NetworksCode0
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelsCode0
FoSR: First-order spectral rewiring for addressing oversquashing in GNNsCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
Filtration Surfaces for Dynamic Graph ClassificationCode0
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph ClassificationCode0
Molecular Topological Profile (MOLTOP) - Simple and Strong Baseline for Molecular Graph ClassificationCode0
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsCode0
Weisfeiler and Leman Go Neural: Higher-order Graph Neural NetworksCode0
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural NetworksCode0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Conditional Prediction ROC Bands for Graph ClassificationCode0
Towards Dynamic Message Passing on GraphsCode0
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph ClassificationCode0
Attention Models in Graphs: A SurveyCode0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Community Detection with Graph Neural NetworksCode0
Clique pooling for graph classificationCode0
Towards Expressive Graph RepresentationCode0
Fast Tree-Field Integrators: From Low Displacement Rank to Topological TransformersCode0
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph ProximityCode0
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique GraphsCode0
Classification of Temporal Graphs using Persistent HomologyCode0
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal TransportCode0
Smart Vectorizations for Single and Multiparameter PersistenceCode0
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity LearningCode0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
Universal Graph Transformer Self-Attention NetworksCode0
SoGCN: Second-Order Graph Convolutional NetworksCode0
Towards Sparse Hierarchical Graph ClassifiersCode0
Sparse Graph Attention NetworksCode0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
Mutual Information Maximization in Graph Neural NetworksCode0
FIT-GNN: Faster Inference Time for GNNs Using CoarseningCode0
Fast Attributed Graph Embedding via Density of StatesCode0
Network classification with applications to brain connectomicsCode0
Network Embedding Exploration Tool (NEExT)Code0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
Neural Network Graph Similarity Computation Based on Graph FusionCode0
A Simple yet Effective Method for Graph ClassificationCode0
Towards Neural Scaling Laws on GraphsCode0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
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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