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
Evaluating Modules in Graph Contrastive LearningCode1
Graph neural networks and attention-based CNN-LSTM for protein classificationCode1
Semi-Supervised Classification with Graph Convolutional NetworksCode1
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image ClassificationCode1
Shortest Path Networks for Graph Property PredictionCode1
Simple and Deep Graph Convolutional NetworksCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
Total Variation Graph Neural NetworksCode1
Spiking Graph Convolutional NetworksCode1
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture SearchCode1
Graph Pooling for Graph Neural Networks: Progress, Challenges, and OpportunitiesCode1
Node Identifiers: Compact, Discrete Representations for Efficient Graph LearningCode1
Exploring Fake News Detection with Heterogeneous Social Media Context GraphsCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
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
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Federated Graph Classification over Non-IID GraphsCode1
Imbalanced Graph Classification via Graph-of-Graph Neural NetworksCode1
Improving Subgraph Recognition with Variational Graph Information BottleneckCode1
Learning distributed representations of graphs with Geo2DRCode1
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
Model-Agnostic Augmentation for Accurate Graph ClassificationCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based SimilarityCode1
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural NetworksCode1
Edge Representation Learning with HypergraphsCode1
Two-stage Training of Graph Neural Networks for Graph ClassificationCode1
Regularized Optimal Transport Layers for Generalized Global Pooling OperationsCode1
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
A Simple Baseline Algorithm for Graph ClassificationCode0
How Curvature Enhance the Adaptation Power of Framelet GCNsCode0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On GraphsCode0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings AugmentationCode0
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation PoolingCode0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
Higher-Order Message Passing for Glycan Representation LearningCode0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph RepresentationsCode0
EGC2: Enhanced Graph Classification with Easy Graph CompressionCode0
Harnessing the Power of Large Language Model for Uncertainty Aware Graph ProcessingCode0
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language ProcessingCode0
Efficient Mixed Precision Quantization in Graph Neural NetworksCode0
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity LearningCode0
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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
9SAEPoolAccuracy80.36Unverified
10MAGPoolAccuracy80.36Unverified
#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-OAAccuracy86.1Unverified
5WL-OA KernelAccuracy86.1Unverified
6FGW wl h=2 spAccuracy85.82Unverified
7WWLAccuracy85.75Unverified
8DUGNNAccuracy85.5Unverified
9δ-2-LWLAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified