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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–600 of 927 papers

TitleStatusHype
High-Order Pooling for Graph Neural Networks with Tensor Decomposition—0
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification—0
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis—0
Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis—0
Label-invariant Augmentation for Semi-Supervised Graph Classification—0
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower BoundCode0
Spatial-temporal associations representation and application for process monitoring using graph convolution neural network—0
NDGGNET-A Node Independent Gate based Graph Neural Networks—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical GuaranteesCode0
Clustered Graph Matching for Label Recovery and Graph Classification—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
DOTIN: Dropping Task-Irrelevant Nodes for GNNsCode0
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning—0
SoftEdge: Regularizing Graph Classification with Random Soft Edges—0
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link PredictionCode0
Multi-view graph structure learning using subspace merging on Grassmann manifold—0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification—0
Incorporating Heterophily into Graph Neural Networks for Graph ClassificationCode0
Supervised Contrastive Learning with Structure Inference for Graph Classification—0
An explainability framework for cortical surface-based deep learningCode0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Distribution Preserving Graph Representation Learning—0
Automated Data Augmentations for Graph Classification—0
Bayesian Deep Learning for Graphs—0
Relation Regularized Scene Graph Generation—0
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
Fairness Amidst Non-IID Graph Data: A Literature Review—0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification—0
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network—0
Investigating Transfer Learning in Graph Neural Networks—0
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs—0
Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels—0
Cross-Domain Few-Shot Graph ClassificationCode0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows—0
Graph Kernel Neural Networks—0
Multi-scale Graph Convolutional Networks with Self-Attention—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
AutoGEL: An Automated Graph Neural Network with Explicit Link InformationCode0
Graph Adversarial Self-Supervised Learning—0
Graph Neural Networks with Parallel Neighborhood Aggregations for Graph ClassificationCode0
Learnable Structural Semantic Readout for Graph Classification—0
IV-GNN : Interval Valued Data Handling Using Graph Neural Network—0
Structure Representation Learning by Jointly Learning to Pool and Represent—0
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation—0
Inferential SIR-GN: Scalable Graph Representation Learning—0
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All datasetsPROTEINSMUTAGNCI1D&DENZYMESIMDb-BCOLLABPTCIMDb-MNCI109CIFAR10 100kREDDIT-B

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762—Unverified
2HGP-SLAccuracy84.91—Unverified
3rLap (unsupervised)Accuracy84.3—Unverified
4TFGW ADJ (L=2)Accuracy82.9—Unverified
5FIT-GNNAccuracy82.1—Unverified
6DUGNNAccuracy81.7—Unverified
7MEWISPoolAccuracy80.71—Unverified
8CIN++Accuracy80.5—Unverified
9MAGPoolAccuracy80.36—Unverified
10SAEPoolAccuracy80.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100—Unverified
2MEWISPoolAccuracy96.66—Unverified
3TFGW ADJ (L=2)Accuracy96.4—Unverified
4GIUNetAccuracy95.7—Unverified
5G_InceptionAccuracy95—Unverified
6GICAccuracy94.44—Unverified
7CIN++Accuracy94.4—Unverified
8sGINAccuracy94.14—Unverified
9CANAccuracy94.1—Unverified
10Deep WL SGN(0,1,2)Accuracy93.68—Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1—Unverified
2WKPI-kmeansAccuracy87.2—Unverified
3FGW wl h=4 spAccuracy86.42—Unverified
4WL-OA KernelAccuracy86.1—Unverified
5WL-OAAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8δ-2-LWLAccuracy85.5—Unverified
9DUGNNAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified