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 151–200 of 927 papers

TitleStatusHype
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
Graph data augmentation with Gromow-Wasserstein Barycenters—0
Harnessing the Power of Large Language Model for Uncertainty Aware Graph ProcessingCode0
Learning the mechanisms of network growthCode0
SSHPool: The Separated Subgraph-based Hierarchical Pooling—0
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification—0
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks—0
Molecular Classification Using Hyperdimensional Graph Classification—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
A Differential Geometric View and Explainability of GNN on Evolving Graphs—0
Cooperative Classification and Rationalization for Graph GeneralizationCode0
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning—0
Multi-Scale Subgraph Contrastive Learning—0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Graph Parsing NetworksCode1
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective—0
Verifying message-passing neural networks via topology-based bounds tighteningCode0
An end-to-end attention-based approach for learning on graphsCode2
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model—0
Class-Balanced and Reinforced Active Learning on Graphs—0
SimMLP: Training MLPs on Graphs without SupervisionCode1
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning—0
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question AnsweringCode4
Generalization Error of Graph Neural Networks in the Mean-field RegimeCode0
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute—0
Flexible infinite-width graph convolutional networks and the importance of representation learning—0
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelsCode0
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Topology-Informed Graph TransformerCode1
Towards Neural Scaling Laws on GraphsCode0
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Graph Transformers without Positional Encodings—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks—0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
Tensor-view Topological Graph Neural NetworkCode0
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph ClassificationCode0
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram IntersectionCode0
Contrastive Learning with Negative Sampling Correction—0
On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social NetworksCode0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
View-based Explanations for Graph Neural NetworksCode1
On the Expressive Power of Graph Neural Networks—0
Saliency-Aware Regularized Graph Neural Network—0
On Discprecncies between Perturbation Evaluations of Graph Neural Network AttributionsCode0
Domain Adaptive Graph Classification—0
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
LightGCN: Evaluated and EnhancedCode0
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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
8δ-2-LWLAccuracy85.5—Unverified
9DUGNNAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified