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 851900 of 927 papers

TitleStatusHype
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsCode0
Quantum-based subgraph convolutional neural networks0
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph ProximityCode0
Clique pooling for graph classificationCode0
A Survey on Graph Kernels0
Subgraph Networks with Application to Structural Feature Space Expansion0
Relational Pooling for Graph RepresentationsCode0
Graph Kernels Based on Linear Patterns: Theoretical and Experimental ComparisonsCode0
Learning Vertex Convolutional Networks for Graph Classification0
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations0
Propagation kernels: efficient graph kernels from propagated informationCode0
Graph Neural Networks with convolutional ARMA filtersCode0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification0
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
Spectral Multigraph Networks for Discovering and Fusing Relationships in MoleculesCode0
Discriminative Graph Autoencoder0
Graph Convolutional Neural Networks via Motif-based Attention0
Gaussian-Induced Convolution for Graphs0
A simple yet effective baseline for non-attributed graph classificationCode0
Towards Sparse Hierarchical Graph ClassifiersCode0
Community Detection with Graph Neural NetworksCode0
A Simple Baseline Algorithm for Graph ClassificationCode0
Network Classification Based Structural Analysis of Real Networks and their Model-Generated CounterpartsCode0
Geometric Scattering for Graph Data Analysis0
Weisfeiler and Leman Go Neural: Higher-order Graph Neural NetworksCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Exploiting Edge Features in Graph Neural Networks0
RetGK: Graph Kernels based on Return Probabilities of Random Walks0
Graph Convolutional Neural Networks based on Quantum Vertex Saliency0
SimGNN: A Neural Network Approach to Fast Graph Similarity ComputationCode0
Attention Models in Graphs: A SurveyCode0
Graph Classification using Structural AttentionCode0
When Work Matters: Transforming Classical Network Structures to Graph CNN0
GraKeL: A Graph Kernel Library in PythonCode0
Graph Capsule Convolutional Neural NetworksCode0
Learning Graph-Level Representations with Recurrent Neural NetworksCode0
Change Point Methods on a Sequence of Graphs0
An End-to-End Deep Learning Architecture for Graph ClassificationCode0
Walk-Steered Convolution for Graph Classification0
Kernel Graph Convolutional Neural Nets0
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On GraphsCode0
Residual Gated Graph ConvNetsCode0
Kernel Graph Convolutional Neural NetworksCode0
Deep Graph Attention Model0
Learning Universal Adversarial Perturbations with Generative ModelsCode0
Graph Classification via Deep Learning with Virtual Nodes0
Graph Classification with 2D Convolutional Neural Networks0
Kernel method for persistence diagrams via kernel embedding and weight factorCode0
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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