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 101150 of 927 papers

TitleStatusHype
Reinforced Causal Explainer for Graph Neural NetworksCode1
Graph neural networks and attention-based CNN-LSTM for protein classificationCode1
Graph Pooling for Graph Neural Networks: Progress, Challenges, and OpportunitiesCode1
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image ClassificationCode1
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal TransportCode1
PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph LearningCode1
Model-Agnostic Augmentation for Accurate Graph ClassificationCode1
Graph Masked Autoencoders with TransformersCode1
G-Mixup: Graph Data Augmentation for Graph ClassificationCode1
Differentially Private Graph Classification with GNNsCode1
GRPE: Relative Positional Encoding for Graph TransformerCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
Revisiting Global Pooling through the Lens of Optimal TransportCode1
Representing Long-Range Context for Graph Neural Networks with Global AttentionCode1
KerGNNs: Interpretable Graph Neural Networks with Graph KernelsCode1
Causal Attention for Interpretable and Generalizable Graph ClassificationCode1
Motif Graph Neural NetworkCode1
Improving Subgraph Recognition with Variational Graph Information BottleneckCode1
A New Perspective on the Effects of Spectrum in Graph Neural NetworksCode1
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
Imbalanced Graph Classification via Graph-of-Graph Neural NetworksCode1
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020Code1
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
Adversarial Attacks on Graph Classification via Bayesian OptimisationCode1
Topological Relational Learning on GraphsCode1
Nested Graph Neural NetworksCode1
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and ImplicationsCode1
Well-classified Examples are Underestimated in Classification with Deep Neural NetworksCode1
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image ClassificationCode1
Inference Attacks Against Graph Neural NetworksCode1
Orthogonal Graph Neural NetworksCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
An Empirical Study of Graph Contrastive LearningCode1
Pooling Architecture Search for Graph ClassificationCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed GraphsCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Edge Representation Learning with HypergraphsCode1
Federated Graph Classification over Non-IID GraphsCode1
Weisfeiler and Lehman Go Cellular: CW NetworksCode1
Evaluating Modules in Graph Contrastive LearningCode1
Convergent Graph SolversCode1
Mixup for Node and Graph ClassificationCode1
How Attentive are Graph Attention Networks?Code1
User Preference-aware Fake News DetectionCode1
Permutation-Invariant Variational Autoencoder for Graph-Level Representation LearningCode1
Parameterized Hypercomplex Graph Neural Networks for Graph ClassificationCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
Size-Invariant Graph Representations for Graph Classification ExtrapolationsCode1
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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