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 151200 of 927 papers

TitleStatusHype
Graph Autoencoder for Graph Compression and Representation LearningCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message PassingCode1
Online Graph Dictionary LearningCode1
Enhance Information Propagation for Graph Neural Network by Heterogeneous AggregationsCode1
[Re] Parameterized Explainer for Graph Neural NetworkCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information MechanismCode1
Membership Inference Attack on Graph Neural NetworksCode1
Label Contrastive Coding based Graph Neural Network for Graph ClassificationCode1
The Shapley Value of Classifiers in Ensemble GamesCode1
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New InsightsCode1
Hierarchical Graph Capsule NetworkCode1
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological AttacksCode1
Two-stage Training of Graph Neural Networks for Graph ClassificationCode1
Parameterized Explainer for Graph Neural NetworkCode1
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer ProxiesCode1
Line Graph Neural Networks for Link PredictionCode1
Robust Optimization as Data Augmentation for Large-scale GraphsCode1
Fast Graph Kernel with Optical Random FeaturesCode1
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node RepresentationsCode1
Factorizable Graph Convolutional NetworksCode1
Graph Information Bottleneck for Subgraph RecognitionCode1
Directional Graph NetworksCode1
Graph Cross Networks with Vertex Infomax PoolingCode1
Learning Graph Normalization for Graph Neural NetworksCode1
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
Lifelong Graph LearningCode1
Quaternion Graph Neural NetworksCode1
Detecting Beneficial Feature Interactions for Recommender SystemsCode1
Second-Order Pooling for Graph Neural NetworksCode1
TUDataset: A collection of benchmark datasets for learning with graphsCode1
Multilevel Graph Matching Networks for Deep Graph Similarity LearningCode1
Simple and Deep Graph Convolutional NetworksCode1
Path Integral Based Convolution and Pooling for Graph Neural NetworksCode1
Graph BackdoorCode1
Backdoor Attacks to Graph Neural NetworksCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Improving Graph Neural Network Expressivity via Subgraph Isomorphism CountingCode1
Wasserstein Embedding for Graph LearningCode1
Contrastive Multi-View Representation Learning on GraphsCode1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
Graph Homomorphism ConvolutionCode1
Principal Neighbourhood Aggregation for Graph NetsCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Universal Function Approximation on GraphsCode1
Learning distributed representations of graphs with Geo2DRCode1
Convolutional Kernel Networks for Graph-Structured DataCode1
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on GraphsCode1
COPT: Coordinated Optimal Transport for Graph SketchingCode1
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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