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 651–700 of 927 papers

TitleStatusHype
Factorizable Graph Convolutional NetworksCode1
Towards Expressive Graph RepresentationCode0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
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 Test—0
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 Attacks—0
Revisiting Graph Neural Networks for Link Prediction—0
Learning Graph Normalization for Graph Neural NetworksCode1
GraphCrop: Subgraph Cropping for Graph Classification—0
Contrastive Self-supervised Learning for Graph Classification—0
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing—0
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 Regularization—0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks—0
Quaternion Graph Neural NetworksCode1
PiNet: Attention Pooling for Graph ClassificationCode0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription—0
Detecting Beneficial Feature Interactions for Recommender SystemsCode1
Multi-view adaptive graph convolutions for graph classification—0
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning—0
Second-Order Pooling for Graph Neural NetworksCode1
Robust Hierarchical Graph Classification with Subgraph Attention—0
TUDataset: A collection of benchmark datasets for learning with graphsCode1
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification—0
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 Classification—0
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 Learning—0
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 Maximization—0
Little Ball of Fur: A Python Library for Graph Sampling—0
SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Customized Graph Neural Networks—0
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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
9SAEPoolAccuracy80.36—Unverified
10MAGPoolAccuracy80.36—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-OAAccuracy86.1—Unverified
5WL-OA KernelAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified