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 501550 of 927 papers

TitleStatusHype
Well-classified Examples are Underestimated in Classification with Deep Neural NetworksCode1
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image ClassificationCode1
Fast Attributed Graph Embedding via Density of StatesCode0
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
Inference Attacks Against Graph Neural NetworksCode1
Permute Me Softly: Learning Soft Permutations for Graph RepresentationsCode0
Inductive Lottery Ticket Learning for Graph Neural Networks0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning0
The Infinite Contextual Graph Markov Model0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation0
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction0
Intrusion-Free Graph Mixup0
Geometric Random Walk Graph Neural Networks via Implicit Layers0
G-Mixup: Graph Augmentation for Graph Classification0
Wasserstein Weisfeiler-Lehman Subtree Distance for Graph-Structured Data0
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs0
Metric Learning on Temporal Graphs via Few-Shot Examples0
Edge but not Least: Cross-View Graph Pooling0
Orthogonal Graph Neural NetworksCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
Structural Optimization Makes Graph Classification Simpler and BetterCode0
An Empirical Study of Graph Contrastive LearningCode1
Graph-based Argument Quality Assessment0
Pooling Architecture Search for Graph ClassificationCode1
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks0
Blockchain Phishing Scam Detection via Multi-channel Graph Classification0
Natural Numerical Networks for Natura 2000 habitats classification by satellite images0
Global Self-Attention as a Replacement for Graph ConvolutionCode1
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Compensation Learning0
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks0
EGC2: Enhanced Graph Classification with Easy Graph CompressionCode0
Automated Graph Learning via Population Based Self-Tuning GCN0
Quantum Graph Convolutional Neural Networks0
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
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural NetworksCode0
Weisfeiler and Lehman Go Cellular: CW NetworksCode1
Attacking Graph Classification via Bayesian Optimisation0
Message Passing in Graph Convolution Networks via Adaptive Filter Banks0
On the approximation capability of GNNs in node classification/regression tasksCode0
Evaluating Modules in Graph Contrastive LearningCode1
Graph Domain Adaptation: A Generative View0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange0
Learnable Hypergraph Laplacian for Hypergraph 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
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