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 451500 of 927 papers

TitleStatusHype
Fairness Amidst Non-IID Graph Data: A Literature Review0
G-Mixup: Graph Data Augmentation for Graph ClassificationCode1
Differentially Private Graph Classification with GNNsCode1
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification0
Investigating Transfer Learning in Graph Neural Networks0
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs0
GRPE: Relative Positional Encoding for Graph TransformerCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels0
Revisiting Global Pooling through the Lens of Optimal TransportCode1
Representing Long-Range Context for Graph Neural Networks with Global AttentionCode1
Cross-Domain Few-Shot Graph ClassificationCode0
KerGNNs: Interpretable Graph Neural Networks with Graph KernelsCode1
Motif Graph Neural NetworkCode1
Causal Attention for Interpretable and Generalizable Graph ClassificationCode1
GPS: A Policy-driven Sampling Approach for Graph Representation Learning0
FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows0
Improving Subgraph Recognition with Variational Graph Information BottleneckCode1
Graph Kernel Neural Networks0
A New Perspective on the Effects of Spectrum in Graph Neural NetworksCode1
Multi-scale Graph Convolutional Networks with Self-Attention0
Controversy Detection: a Text and Graph Neural Network Based Approach0
AutoGEL: An Automated Graph Neural Network with Explicit Link InformationCode0
Imbalanced Graph Classification via Graph-of-Graph Neural NetworksCode1
Graph Adversarial Self-Supervised Learning0
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020Code1
Graph Neural Networks with Parallel Neighborhood Aggregations for Graph ClassificationCode0
Learnable Structural Semantic Readout for Graph Classification0
IV-GNN : Interval Valued Data Handling Using Graph Neural Network0
Structure Representation Learning by Jointly Learning to Pool and Represent0
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation0
Inferential SIR-GN: Scalable Graph Representation Learning0
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices0
Adversarial Attacks on Graph Classification via Bayesian OptimisationCode1
Multi network InfoMax: A pre-training method involving graph convolutional networks0
Topological Relational Learning on GraphsCode1
InfoGCL: Information-Aware Graph Contrastive Learning0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks0
Nested Graph Neural NetworksCode1
Watermarking Graph Neural Networks based on Backdoor Attacks0
Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos0
Capsule Graph Neural Networks with EM Routing0
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs0
ifMixup: Interpolating Graph Pair to Regularize Graph Classification0
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and ImplicationsCode1
SoGCN: Second-Order Graph Convolutional NetworksCode0
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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
8δ-2-LWLAccuracy85.5Unverified
9DUGNNAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified