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 451–500 of 927 papers

TitleStatusHype
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph CoarseningCode0
Explainable and Position-Aware Learning in Digital Pathology—0
Self-supervised Learning and Graph Classification under Heterophily—0
Analysis and Approximate Inference of Large Random Kronecker GraphsCode0
Graph Mixup with Soft Alignments—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
XInsight: Revealing Model Insights for GNNs with Flow-based Explanations—0
Randomized Schur Complement Views for Graph Contrastive LearningCode0
Graph Classification Gaussian Processes via Spectral Features—0
Explaining and Adapting Graph Conditional Shift—0
Message-passing selection: Towards interpretable GNNs for graph classification—0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
Towards Semi-supervised Universal Graph Classification—0
Is Rewiring Actually Helpful in Graph Neural Networks?—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies—0
Robust Ante-hoc Graph Explainer using Bilevel Optimization—0
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding—0
Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective—0
Sequential Graph Neural Networks for Source Code Vulnerability Identification—0
DEGREE: Decomposition Based Explanation For Graph Neural NetworksCode0
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive LearningCode0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Deep Graph Reprogramming—0
TGNN: A Joint Semi-supervised Framework for Graph-level Classification—0
ID-MixGCL: Identity Mixup for Graph Contrastive Learning—0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
Transformer and Snowball Graph Convolution Learning for Brain functional network ClassificationCode0
Topological Pooling on GraphsCode0
Structural Imbalance Aware Graph Augmentation Learning—0
A Comparison of Graph Neural Networks for Malware Classification—0
MPool: Motif-Based Graph Pooling—0
Graph Positional Encoding via Random Feature Propagation—0
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks—0
Diffusing Graph Attention—0
A semantic backdoor attack against Graph Convolutional Networks—0
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning—0
On the Expressivity of Persistent Homology in Graph Learning—0
Search to Capture Long-range Dependency with Stacking GNNs for Graph ClassificationCode0
From Graph Generation to Graph Classification—0
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search—0
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network—0
Structural Explanations for Graph Neural Networks using HSIC—0
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data—0
Task-Agnostic Graph Neural Network Evaluation via Adversarial CollaborationCode0
Graph Scattering beyond Wavelet Shackles—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer—0
GANExplainer: GAN-based Graph Neural Networks Explainer—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Show:102550
← PrevPage 10 of 19Next →
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
8δ-2-LWLAccuracy85.5—Unverified
9DUGNNAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified