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 301–350 of 927 papers

TitleStatusHype
Discriminative Graph Autoencoder—0
Graph Pooling by Edge Cut—0
Diffusing Graph Attention—0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation—0
Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network—0
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks—0
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph—0
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model—0
Blockchain Phishing Scam Detection via Multi-channel Graph Classification—0
BLIS-Net: Classifying and Analyzing Signals on Graphs—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification—0
Graphs in machine learning: an introduction—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics—0
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search—0
Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy—0
Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images—0
Graph Neural Networks at a Fraction—0
Degree-Conscious Spiking Graph for Cross-Domain Adaptation—0
Beyond Homophily with Graph Echo State Networks—0
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning—0
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability—0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction—0
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification—0
Deep Graph Reprogramming—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network—0
Deep Graph Kernels—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview—0
Deep Graph Attention Model—0
Class-Balanced and Reinforced Active Learning on Graphs—0
DEEP GEOMETRICAL GRAPH CLASSIFICATION—0
Bayesian Deep Learning for Graphs—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-based Argument Quality Assessment—0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation—0
Graph Classification Based on Skeleton and Component Features—0
Graph-Aware Transformer: Is Attention All Graphs Need?—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