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
Anonymous Walk EmbeddingsCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Recurrent Distance Filtering for Graph Representation LearningCode1
Reinforced Causal Explainer for Graph Neural NetworksCode1
Gated Graph Sequence Neural NetworksCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Segmented Graph-Bert for Graph Instance ModelingCode1
Semi-Supervised Classification with Graph Convolutional NetworksCode1
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image ClassificationCode1
Simple and Deep Graph Convolutional NetworksCode1
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural NetworksCode1
FAITH: Few-Shot Graph Classification with Hierarchical Task GraphsCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Variational Recurrent Neural Networks for Graph ClassificationCode1
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
Approximate Network Motif Mining Via Graph LearningCode1
Structural Entropy Guided Graph Hierarchical PoolingCode1
SimMLP: Training MLPs on Graphs without SupervisionCode1
Structure-Feature based Graph Self-adaptive PoolingCode1
Graph Pooling for Graph Neural Networks: Progress, Challenges, and OpportunitiesCode1
Inference Attacks Against Graph Neural NetworksCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Global Counterfactual Explainer for Graph Neural NetworksCode1
Transforming PageRank into an Infinite-Depth Graph Neural NetworkCode1
Edge Representation Learning with HypergraphsCode1
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural NetworksCode1
Parameterized Hypercomplex Graph Neural Networks for Graph ClassificationCode1
Graph2Graph Learning with Conditional Autoregressive Models0
A Comparison of Graph Neural Networks for Malware Classification0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin0
Gradient Inversion Attack on Graph Neural Networks0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models0
Graph Adversarial Self-Supervised Learning0
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks0
A semantic backdoor attack against Graph Convolutional Networks0
GQWformer: A Quantum-based Transformer for Graph Representation Learning0
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention0
EMP: Effective Multidimensional Persistence for Graph Representation Learning0
Ego-based Entropy Measures for Structural Representations on Graphs0
CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges0
Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques0
Classification by Attention: Scene Graph Classification with Prior Knowledge0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost0
Ego-based Entropy Measures for Structural Representations0
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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