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 151200 of 927 papers

TitleStatusHype
Graph Parsing NetworksCode1
Graph Pooling for Graph Neural Networks: Progress, Challenges, and OpportunitiesCode1
Robust Optimization as Data Augmentation for Large-scale GraphsCode1
GRPE: Relative Positional Encoding for Graph TransformerCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and GeneralizabilityCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal TransportCode1
Automatic Relation-aware Graph Network ProliferationCode1
Energy TransformerCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
Enhance Information Propagation for Graph Neural Network by Heterogeneous AggregationsCode1
How Powerful are Graph Neural Networks?Code1
BAGEL: A Benchmark for Assessing Graph Neural Network ExplanationsCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on GraphsCode1
A New Perspective on the Effects of Spectrum in Graph Neural NetworksCode1
Total Variation Graph Neural NetworksCode1
Causal Attention for Interpretable and Generalizable Graph ClassificationCode1
Inductive Representation Learning on Large GraphsCode1
Inference Attacks Against Graph Neural NetworksCode1
Adversarial Attacks on Graph Classification via Bayesian OptimisationCode1
Efficiently predicting high resolution mass spectra with graph neural networksCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Learning Graph Normalization for Graph Neural NetworksCode1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
LightGCN: Simplifying and Powering Graph Convolution Network for RecommendationCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node RepresentationsCode1
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Memory-Based Graph NetworksCode1
Metric Based Few-Shot Graph ClassificationCode1
Mixup for Node and Graph ClassificationCode1
Model-Agnostic Augmentation for Accurate Graph ClassificationCode1
Modeling Relational Data with Graph Convolutional NetworksCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Detecting Beneficial Feature Interactions for Recommender SystemsCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer ProxiesCode1
Online Graph Dictionary LearningCode1
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
Differentially Private Graph Classification with GNNsCode1
Orthogonal Graph Neural NetworksCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
Directional Graph NetworksCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
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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