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 1–50 of 927 papers

TitleStatusHype
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question AnsweringCode4
Molecular Fingerprints Are Strong Models for Peptide Function PredictionCode3
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline KernelsCode3
Hypergraph Isomorphism ComputationCode2
Do Transformers Really Perform Bad for Graph Representation?Code2
Dynamic GNNs for Precise Seizure Detection and Classification from EEG DataCode2
An end-to-end attention-based approach for learning on graphsCode2
One for All: Towards Training One Graph Model for All Classification TasksCode2
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet ExcellenceCode2
Recipe for a General, Powerful, Scalable Graph TransformerCode2
GraphMAE: Self-Supervised Masked Graph AutoencodersCode2
Gradformer: Graph Transformer with Exponential DecayCode2
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPTCode2
Graph Neural Networks in TensorFlow and Keras with SpektralCode2
Identity-aware Graph Neural NetworksCode2
KAGNNs: Kolmogorov-Arnold Networks meet Graph LearningCode2
Towards Explanation for Unsupervised Graph-Level Representation LearningCode2
Benchmarking Graph Neural NetworksCode2
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product NetworksCode2
Optimal Transport for structured data with application on graphsCode2
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
DRew: Dynamically Rewired Message Passing with DelayCode1
Automatic Relation-aware Graph Network ProliferationCode1
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020Code1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Agent-based Graph Neural NetworksCode1
BAGEL: A Benchmark for Assessing Graph Neural Network ExplanationsCode1
AutoRDF2GML: Facilitating RDF Integration in Graph Machine LearningCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Directional Graph NetworksCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
Causal Attention for Interpretable and Generalizable Graph ClassificationCode1
Convolutional Kernel Networks for Graph-Structured DataCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
An Empirical Study of Graph Contrastive LearningCode1
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on GraphsCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Adversarial Attacks on Graph Classification via Bayesian OptimisationCode1
Convergent Graph SolversCode1
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
COPT: Coordinated Optimal Transport for Graph SketchingCode1
Composition-based Multi-Relational Graph Convolutional NetworksCode1
Backdoor Attacks to Graph Neural NetworksCode1
Anonymous Walk EmbeddingsCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
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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
9SAEPoolAccuracy80.36—Unverified
10MAGPoolAccuracy80.36—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-OAAccuracy86.1—Unverified
5WL-OA KernelAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified