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 401–450 of 927 papers

TitleStatusHype
0/1 Deep Neural Networks via Block Coordinate Descent—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
Independence Promoted Graph Disentangled Networks—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Graph Mixup with Soft Alignments—0
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification—0
Graph Adversarial Self-Supervised Learning—0
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective—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
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
Inferential SIR-GN: Scalable Graph Representation Learning—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 Neural Network with Curriculum Learning for Imbalanced Node Classification—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Gradient Inversion Attack on Graph Neural Networks—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Graph Out-of-Distribution Generalization with Controllable Data Augmentation—0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Domain Adaptive Graph Classification—0
Graph Pooling by Edge Cut—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
Graph Positional Encoding via Random Feature Propagation—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Going beyond persistent homology using persistent homology—0
Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription—0
G-Mixup: Graph Augmentation for Graph Classification—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing—0
Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification—0
Graph Structural Aggregation for Explainable Learning—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
How hard is to distinguish graphs with graph neural networks?—0
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation—0
Geometric Scattering for Graph Data Analysis—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