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

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Showing 301–325 of 927 papers

TitleStatusHype
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 Kernels via Functional Embedding—0
Graph Mixup with Soft Alignments—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—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
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network—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
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—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 embedding using multi-layer adjacent point merging model—0
Graph-Graph Similarity Network—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