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
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
Exphormer: Sparse Transformers for GraphsCode1
COPT: Coordinated Optimal Transport for Graph SketchingCode1
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and GeneralizabilityCode1
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020Code1
Expander Graph PropagationCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
Improving Subgraph Recognition with Variational Graph Information BottleneckCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Exploring Fake News Detection with Heterogeneous Social Media Context GraphsCode1
Automatic Relation-aware Graph Network ProliferationCode1
Federated Graph Classification over Non-IID GraphsCode1
AutoRDF2GML: Facilitating RDF Integration in Graph Machine LearningCode1
Factorizable Graph Convolutional NetworksCode1
Backdoor Attacks to Graph Neural NetworksCode1
Learning distributed representations of graphs with Geo2DRCode1
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
LightGCN: Simplifying and Powering Graph Convolution Network for RecommendationCode1
An Empirical Study of Graph Contrastive LearningCode1
Causal Attention for Interpretable and Generalizable Graph ClassificationCode1
DRew: Dynamically Rewired Message Passing with DelayCode1
Fast Graph Kernel with Optical Random FeaturesCode1
Adversarial Attacks on Graph Classification via Bayesian OptimisationCode1
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer ProxiesCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Metric Based Few-Shot Graph ClassificationCode1
Graph Masked Autoencoders with TransformersCode1
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal TransportCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Total Variation Graph Neural NetworksCode1
Model-Agnostic Augmentation for Accurate Graph ClassificationCode1
Modeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite MatricesCode1
Motif Graph Neural NetworkCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Global Counterfactual Explainer for Graph Neural NetworksCode1
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link PredictionCode1
Detecting Beneficial Feature Interactions for Recommender SystemsCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Graph BackdoorCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Differentially Private Graph Classification with GNNsCode1
Graph Attention NetworksCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
Directional Graph NetworksCode1
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
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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
9SAEPoolAccuracy80.36Unverified
10MAGPoolAccuracy80.36Unverified
#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-OAAccuracy86.1Unverified
5WL-OA KernelAccuracy86.1Unverified
6FGW wl h=2 spAccuracy85.82Unverified
7WWLAccuracy85.75Unverified
8DUGNNAccuracy85.5Unverified
9δ-2-LWLAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified