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 201250 of 927 papers

TitleStatusHype
Anonymous Walk EmbeddingsCode1
Graph Neural Distance Metric Learning with Graph-BertCode1
Graph neural networks and attention-based CNN-LSTM for protein classificationCode1
Total Variation Graph Neural NetworksCode1
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image ClassificationCode1
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image ClassificationCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Graph Information Bottleneck for Subgraph RecognitionCode1
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
S-Mixup: Structural Mixup for Graph Neural NetworksCode1
Exploring Fake News Detection with Heterogeneous Social Media Context GraphsCode1
Graph Parsing NetworksCode1
Streaming Graph Neural NetworksCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Federated Graph Classification over Non-IID GraphsCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
DRew: Dynamically Rewired Message Passing with DelayCode1
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
Improving Graph Neural Network Expressivity via Subgraph Isomorphism CountingCode1
GRPE: Relative Positional Encoding for Graph TransformerCode1
KerGNNs: Interpretable Graph Neural Networks with Graph KernelsCode1
Topological Relational Learning on GraphsCode1
Spectral Clustering with Graph Neural Networks for Graph PoolingCode1
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Recurrent Distance Filtering for Graph Representation LearningCode1
TUDataset: A collection of benchmark datasets for learning with graphsCode1
Edge Representation Learning with HypergraphsCode1
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity AnalysisCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Clique pooling for graph classificationCode0
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique GraphsCode0
A simple yet effective baseline for non-attributed graph classificationCode0
Classification of Temporal Graphs using Persistent HomologyCode0
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language ProcessingCode0
A Simple Baseline Algorithm for Graph ClassificationCode0
How Curvature Enhance the Adaptation Power of Framelet GCNsCode0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings AugmentationCode0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation PoolingCode0
Higher-Order Message Passing for Glycan Representation LearningCode0
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph RepresentationsCode0
EGC2: Enhanced Graph Classification with Easy Graph CompressionCode0
Hard Label Black Box Node Injection Attack on Graph Neural NetworksCode0
Efficient Mixed Precision Quantization in Graph Neural NetworksCode0
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity LearningCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
GSTAM: Efficient Graph Distillation with Structural Attention-MatchingCode0
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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