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 501–550 of 927 papers

TitleStatusHype
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics—0
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
PropEnc: A Property Encoder for Graph Neural Networks—0
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks—0
A semantic backdoor attack against Graph Convolutional Networks—0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
A Survey on Graph Classification and Link Prediction based on GNN—0
A Survey on Graph Kernels—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
Asynchronous Neural Networks for Learning in Graphs—0
Attacking Graph Classification via Bayesian Optimisation—0
Attacking Graph Convolutional Networks via Rewiring—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Automated Data Augmentations for Graph Classification—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation—0
Bayesian Deep Learning for Graphs—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning—0
Beyond Homophily with Graph Echo State Networks—0
Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images—0
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search—0
BLIS-Net: Classifying and Analyzing Signals on Graphs—0
Blockchain Phishing Scam Detection via Multi-channel Graph Classification—0
Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network—0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation—0
Bridging Graph Network to Lifelong Learning with Feature Interaction—0
Cached Operator Reordering: A Unified View for Fast GNN Training—0
Capsule Graph Neural Networks with EM Routing—0
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations—0
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks—0
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing—0
Change Point Methods on a Sequence of Graphs—0
CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
Clustered Graph Matching for Label Recovery and Graph Classification—0
Cluster-guided Contrastive Class-imbalanced Graph Classification—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
Coloring graph neural networks for node disambiguation—0
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning—0
Compensation Learning—0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks—0
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents—0
Conditional Local Feature Encoding for Graph Neural Networks—0
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks—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