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 201250 of 927 papers

TitleStatusHype
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
Segmented Graph-Bert for Graph Instance ModelingCode1
Graph Neural Distance Metric Learning with Graph-BertCode1
LightGCN: Simplifying and Powering Graph Convolution Network for RecommendationCode1
Structure-Feature based Graph Self-adaptive PoolingCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity AnalysisCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Hierarchical Graph Pooling with Structure LearningCode1
Composition-based Multi-Relational Graph Convolutional NetworksCode1
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information MaximizationCode1
Spectral Clustering with Graph Neural Networks for Graph PoolingCode1
GOT: An Optimal Transport framework for Graph comparisonCode1
Strategies for Pre-training Graph Neural NetworksCode1
GNNExplainer: Generating Explanations for Graph Neural NetworksCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
Fake News Detection on Social Media using Geometric Deep LearningCode1
Variational Recurrent Neural Networks for Graph ClassificationCode1
Streaming Graph Neural NetworksCode1
How Powerful are Graph Neural Networks?Code1
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
Anonymous Walk EmbeddingsCode1
Graph Attention NetworksCode1
graph2vec: Learning Distributed Representations of GraphsCode1
Inductive Representation Learning on Large GraphsCode1
Modeling Relational Data with Graph Convolutional NetworksCode1
Semi-Supervised Classification with Graph Convolutional NetworksCode1
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large GraphsCode1
Gated Graph Sequence Neural NetworksCode1
Density-aware Walks for Coordinated Campaign DetectionCode0
Positional Encoding meets Persistent Homology on GraphsCode0
Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs0
Graph Style Transfer for Counterfactual ExplainabilityCode0
Scalable Graph Generative Modeling via Substructure SequencesCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
Schreier-Coset Graph Propagation0
Rhomboid Tiling for Geometric Graph Deep Learning0
Efficient Mixed Precision Quantization in Graph Neural NetworksCode0
DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models0
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean DatasetsCode0
Graph Fourier Transformer with Structure-Frequency InformationCode0
GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learningCode0
LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisation0
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured DataCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary PerturbationsCode0
Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks0
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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
8δ-2-LWLAccuracy85.5Unverified
9DUGNNAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified