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 651–700 of 927 papers

TitleStatusHype
Class-Balanced and Reinforced Active Learning on Graphs—0
Graph Classification Based on Skeleton and Component Features—0
Graph Classification by Mixture of Diverse Experts—0
Graph Classification Gaussian Processes via Spectral Features—0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Graph Classification via Deep Learning with Virtual Nodes—0
Graph Classification via Discriminative Edge Feature Learning—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
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations—0
Graph Convolutional Neural Networks based on Quantum Vertex Saliency—0
Graph Convolutional Neural Networks via Motif-based Attention—0
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization—0
Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos—0
GraphCrop: Subgraph Cropping for Graph Classification—0
Graph data augmentation with Gromow-Wasserstein Barycenters—0
Graph Domain Adaptation: A Generative View—0
Graph embedding using multi-layer adjacent point merging model—0
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Graph-Graph Similarity Network—0
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection—0
Graph Invariant Kernels—0
Graph Kernel Neural Networks—0
Graph Kernels: A Survey—0
Graph Kernels via Functional Embedding—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Graphlet-based lazy associative graph classification—0
Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks—0
GraphMDN: Leveraging graph structure and deep learning to solve inverse problems—0
Graph Mixup with Soft Alignments—0
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification—0
Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis—0
Graph Neural Networks at a Fraction—0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview—0
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning—0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification—0
Graph Out-of-Distribution Generalization with Controllable Data Augmentation—0
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs—0
Graph Pooling by Edge Cut—0
Graph Pooling with Node Proximity for Hierarchical Representation Learning—0
Graph Positional Encoding via Random Feature Propagation—0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs—0
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement—0
Graph Scattering beyond Wavelet Shackles—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
Graphs in machine learning: an introduction—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