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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 927 papers

TitleStatusHype
Class-Balanced and Reinforced Active Learning on Graphs—0
DEEP GEOMETRICAL GRAPH CLASSIFICATION—0
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network—0
Graph Classification via Deep Learning with Virtual Nodes—0
Bayesian Deep Learning for Graphs—0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
Graph Classification with 2D Convolutional Neural Networks—0
Graph Classification with Geometric Scattering—0
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction—0
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations—0
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning—0
Graph Convolutional Neural Networks based on Quantum Vertex Saliency—0
Graph-based Argument Quality Assessment—0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation—0
Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos—0
GraphCrop: Subgraph Cropping for Graph Classification—0
Graph-Aware Transformer: Is Attention All Graphs Need?—0
Graph data augmentation with Gromow-Wasserstein Barycenters—0
Degree-Conscious Spiking Graph for Cross-Domain Adaptation—0
Graph Domain Adaptation: A Generative View—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Graph embedding using multi-layer adjacent point merging model—0
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
Graph-Graph Similarity Network—0
Graph Transformers without Positional Encodings—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Graph Adversarial Self-Supervised Learning—0
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation—0
Graph2Graph Learning with Conditional Autoregressive Models—0
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks—0
Graph Invariant Kernels—0
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Graph Kernels: A Survey—0
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks—0
Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network—0
Graph Kernels via Functional Embedding—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Gradient Inversion Attack on Graph Neural Networks—0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
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All datasetsPROTEINSMUTAGNCI1D&DENZYMESIMDb-BCOLLABPTCIMDb-MNCI109CIFAR10 100kREDDIT-B

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
9MAGPoolAccuracy80.36—Unverified
10SAEPoolAccuracy80.36—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-OA KernelAccuracy86.1—Unverified
5WL-OAAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified