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 5175 of 927 papers

TitleStatusHype
CIN++: Enhancing Topological Message PassingCode1
Exploring Fake News Detection with Heterogeneous Social Media Context GraphsCode1
CKGConv: General Graph Convolution with Continuous KernelsCode1
Approximate Network Motif Mining Via Graph LearningCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Total Variation Graph Neural NetworksCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Federated Graph Classification over Non-IID GraphsCode1
Agent-based Graph Neural NetworksCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
Composition-based Multi-Relational Graph Convolutional NetworksCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological AttacksCode1
Enhance Information Propagation for Graph Neural Network by Heterogeneous AggregationsCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
Differentially Private Graph Classification with GNNsCode1
Directional Graph NetworksCode1
DRew: Dynamically Rewired Message Passing with DelayCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Backdoor Attacks to Graph Neural NetworksCode1
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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
8DUGNNAccuracy85.5Unverified
9δ-2-LWLAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified