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 501–550 of 927 papers

TitleStatusHype
Labeled Graph Generative Adversarial Networks—0
Label-invariant Augmentation for Semi-Supervised Graph Classification—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
Learnable Filters for Geometric Scattering Modules—0
Learnable Structural Semantic Readout for Graph Classification—0
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification—0
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute—0
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning—0
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling—0
Learning Graph Neural Networks with Noisy Labels—0
Learning Graph Representations—0
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters—0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Learning Vertex Convolutional Networks for Graph Classification—0
LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisation—0
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning—0
Little Ball of Fur: A Python Library for Graph Sampling—0
LookHops: light multi-order convolution and pooling for graph classification—0
MADE: Graph Backdoor Defense with Masked Unlearning—0
Maximal Independent Vertex Set applied to Graph Pooling—0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network—0
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning—0
Message Passing in Graph Convolution Networks via Adaptive Filter Banks—0
Message-passing selection: Towards interpretable GNNs for graph classification—0
Metric Learning on Temporal Graphs via Few-Shot Examples—0
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification—0
Mincut Pooling in Graph Neural Networks—0
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Molecular Classification Using Hyperdimensional Graph Classification—0
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference—0
Motif-driven Subgraph Structure Learning for Graph Classification—0
MPool: Motif-Based Graph Pooling—0
MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
Multi-Channel Graph Convolutional Networks—0
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities—0
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning—0
Multi network InfoMax: A pre-training method involving graph convolutional networks—0
Multi-scale Graph Convolutional Networks with Self-Attention—0
Multi-Scale Subgraph Contrastive Learning—0
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms—0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
Multi-view adaptive graph convolutions for graph classification—0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
Multi-view graph structure learning using subspace merging on Grassmann manifold—0
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Natural Numerical Networks for Natura 2000 habitats classification by satellite images—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