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 651700 of 927 papers

TitleStatusHype
Factorizable Graph Convolutional NetworksCode1
Towards Expressive Graph RepresentationCode0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling0
Graph Information Bottleneck for Subgraph RecognitionCode1
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node RepresentationsCode1
Understanding the Power of Persistence Pairing via Permutation Test0
Directional Graph NetworksCode1
Data-Driven Learning of Geometric Scattering NetworksCode0
Graph Cross Networks with Vertex Infomax PoolingCode1
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks0
Revisiting Graph Neural Networks for Link Prediction0
Learning Graph Normalization for Graph Neural NetworksCode1
GraphCrop: Subgraph Cropping for Graph Classification0
Contrastive Self-supervised Learning for Graph Classification0
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing0
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
Lifelong Graph LearningCode1
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks0
Quaternion Graph Neural NetworksCode1
PiNet: Attention Pooling for Graph ClassificationCode0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks0
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription0
Detecting Beneficial Feature Interactions for Recommender SystemsCode1
Multi-view adaptive graph convolutions for graph classification0
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning0
Second-Order Pooling for Graph Neural NetworksCode1
Robust Hierarchical Graph Classification with Subgraph Attention0
TUDataset: A collection of benchmark datasets for learning with graphsCode1
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification0
Multilevel Graph Matching Networks for Deep Graph Similarity LearningCode1
Simple and Deep Graph Convolutional NetworksCode1
A Novel Higher-order Weisfeiler-Lehman Graph ConvolutionCode0
Path Integral Based Convolution and Pooling for Graph Neural NetworksCode1
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification0
Graph Neural Networks in TensorFlow and Keras with SpektralCode2
Graph BackdoorCode1
TreeRNN: Topology-Preserving Deep GraphEmbedding and LearningCode0
Backdoor Attacks to Graph Neural NetworksCode1
Graph Pooling with Node Proximity for Hierarchical Representation Learning0
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Wasserstein Embedding for Graph LearningCode1
Improving Graph Neural Network Expressivity via Subgraph Isomorphism CountingCode1
Contrastive Multi-View Representation Learning on GraphsCode1
Graph-Aware Transformer: Is Attention All Graphs Need?0
Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization0
Little Ball of Fur: A Python Library for Graph Sampling0
SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks0
Customized Graph Neural Networks0
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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