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 851–900 of 927 papers

TitleStatusHype
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsCode0
Quantum-based subgraph convolutional neural networks—0
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph ProximityCode0
Clique pooling for graph classificationCode0
A Survey on Graph Kernels—0
Subgraph Networks with Application to Structural Feature Space Expansion—0
Relational Pooling for Graph RepresentationsCode0
Graph Kernels Based on Linear Patterns: Theoretical and Experimental ComparisonsCode0
Learning Vertex Convolutional Networks for Graph Classification—0
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations—0
Propagation kernels: efficient graph kernels from propagated informationCode0
Graph Neural Networks with convolutional ARMA filtersCode0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation—0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
Spectral Multigraph Networks for Discovering and Fusing Relationships in MoleculesCode0
Discriminative Graph Autoencoder—0
Graph Convolutional Neural Networks via Motif-based Attention—0
Gaussian-Induced Convolution for Graphs—0
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 Analysis—0
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 Networks—0
RetGK: Graph Kernels based on Return Probabilities of Random Walks—0
Graph Convolutional Neural Networks based on Quantum Vertex Saliency—0
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 CNN—0
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 Graphs—0
An End-to-End Deep Learning Architecture for Graph ClassificationCode0
Walk-Steered Convolution for Graph Classification—0
Kernel Graph Convolutional Neural Nets—0
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model—0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On GraphsCode0
Residual Gated Graph ConvNetsCode0
Kernel Graph Convolutional Neural NetworksCode0
Deep Graph Attention Model—0
Learning Universal Adversarial Perturbations with Generative ModelsCode0
Graph Classification via Deep Learning with Virtual Nodes—0
Graph Classification with 2D Convolutional Neural Networks—0
Kernel method for persistence diagrams via kernel embedding and weight factorCode0
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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