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 701–750 of 927 papers

TitleStatusHype
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection—0
How hard is to distinguish graphs with graph neural networks?—0
Ring Reservoir Neural Networks for Graphs—0
Graph Homomorphism ConvolutionCode1
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network—0
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Principal Neighbourhood Aggregation for Graph NetsCode1
Pooling in Graph Convolutional Neural Networks—0
Geometrically Principled Connections in Graph Neural Networks—0
Learning Deep Graph Representations via Convolutional Neural NetworksCode0
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
A Collective Learning Framework to Boost GNN Expressiveness—0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Universal Function Approximation on GraphsCode1
Learning distributed representations of graphs with Geo2DRCode1
Wasserstein-based Graph Alignment—0
Convolutional Kernel Networks for Graph-Structured DataCode1
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on GraphsCode1
COPT: Coordinated Optimal Transport for Graph SketchingCode1
Benchmarking Graph Neural NetworksCode2
Ego-based Entropy Measures for Structural Representations—0
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Memory-Based Graph NetworksCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Graph Neural Distance Metric Learning with Graph-BertCode1
Segmented Graph-Bert for Graph Instance ModelingCode1
LightGCN: Simplifying and Powering Graph Convolution Network for RecommendationCode1
Distance Metric Learning for Graph Structured DataCode0
Structure-Feature based Graph Self-adaptive PoolingCode1
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features—0
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Understanding the Power of Persistence Pairing via Permutation Test—0
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction—0
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity AnalysisCode1
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal TransportCode0
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Multi-Channel Graph Convolutional Networks—0
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis—0
Coloring graph neural networks for node disambiguation—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Sparse Graph Attention NetworksCode0
Rethinking the Item Order in Session-based Recommendation with Graph Neural NetworksCode0
Independence Promoted Graph Disentangled Networks—0
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding—0
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph RepresentationsCode0
Hierarchical Graph Pooling with Structure LearningCode1
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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