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 301350 of 927 papers

TitleStatusHype
MPool: Motif-Based Graph Pooling0
Graph Positional Encoding via Random Feature Propagation0
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks0
Diffusing Graph Attention0
A semantic backdoor attack against Graph Convolutional Networks0
Masked Relation Learning for DeepFake DetectionCode1
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning0
On the Expressivity of Persistent Homology in Graph Learning0
Search to Capture Long-range Dependency with Stacking GNNs for Graph ClassificationCode0
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical StructuresCode1
From Graph Generation to Graph Classification0
Energy TransformerCode1
Unnoticeable Backdoor Attacks on Graph Neural NetworksCode1
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search0
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network0
Structural Explanations for Graph Neural Networks using HSIC0
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data0
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
On the Connection Between MPNN and Graph TransformerCode1
Task-Agnostic Graph Neural Network Evaluation via Adversarial CollaborationCode0
Efficiently predicting high resolution mass spectra with graph neural networksCode1
DBGDGM: Dynamic Brain Graph Deep Generative Model0
Graph Scattering beyond Wavelet Shackles0
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer0
GANExplainer: GAN-based Graph Neural Networks Explainer0
A Generalization of ViT/MLP-Mixer to GraphsCode1
Regularized Optimal Transport Layers for Generalized Global Pooling OperationsCode1
Exploring Fake News Detection with Heterogeneous Social Media Context GraphsCode1
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges0
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification0
Total Variation Graph Neural NetworksCode1
Application of Graph Neural Networks and graph descriptors for graph classification0
Graph Contrastive Learning with Implicit AugmentationsCode0
Unlearning Graph Classifiers with Limited Data ResourcesCode1
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification0
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations0
Improving Graph Neural Networks with Learnable Propagation Operators0
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
Graph Fuzzy System: Concepts, Models and AlgorithmsCode0
Beyond Homophily with Graph Echo State Networks0
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex ClusteringCode0
Efficient Automatic Machine Learning via Design GraphsCode0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning0
Global Counterfactual Explainer for Graph Neural NetworksCode1
FoSR: First-order spectral rewiring for addressing oversquashing in GNNsCode0
Test-Time Training for Graph Neural Networks0
MGNNI: Multiscale Graph Neural Networks with Implicit LayersCode1
Boosting Graph Neural Networks via Adaptive Knowledge Distillation0
Pooling Strategies for Simplicial Convolutional NetworksCode0
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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