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 601–650 of 927 papers

TitleStatusHype
Learning Parametrised Graph Shift OperatorsCode0
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information MechanismCode1
GraphAttacker: A General Multi-Task GraphAttack FrameworkCode0
Membership Inference Attack on Graph Neural NetworksCode1
Label Contrastive Coding based Graph Neural Network for Graph ClassificationCode1
Optimisation of Spectral Wavelets for Persistence-based Graph Classification—0
The Shapley Value of Classifiers in Ensemble GamesCode1
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement—0
Graph Pooling by Edge Cut—0
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations—0
Neural Pooling for Graph Neural Networks—0
Graph Structural Aggregation for Explainable Learning—0
Bridging Graph Network to Lifelong Learning with Feature Interaction—0
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling—0
Graph-Graph Similarity Network—0
One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting—0
On Single-environment Extrapolations in Graph Classification and Regression Tasks—0
Polynomial Graph Convolutional Networks—0
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning—0
LookHops: light multi-order convolution and pooling for graph classification—0
Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification—0
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New InsightsCode1
An Experimental Study of the Transferability of Spectral Graph NetworksCode0
Hierarchical Graph Capsule NetworkCode1
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs—0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning—0
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric SpaceCode0
COPT: Coordinated Optimal Transport on Graphs—0
Random Walk Graph Neural Networks—0
A graph similarity for deep learning—0
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological AttacksCode1
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Two-stage Training of Graph Neural Networks for Graph ClassificationCode1
Parameterized Explainer for Graph Neural NetworkCode1
Sampling and Recovery of Graph Signals based on Graph Neural Networks—0
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents—0
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer—0
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation LearningCode0
Graph embedding using multi-layer adjacent point merging model—0
GraphMDN: Leveraging graph structure and deep learning to solve inverse problems—0
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer ProxiesCode1
Co-embedding of Nodes and Edges with Graph Neural Networks—0
Line Graph Neural Networks for Link PredictionCode1
Robust Optimization as Data Augmentation for Large-scale GraphsCode1
Topology-Aware Graph Pooling Networks—0
K-plex Cover Pooling for Graph Neural Networks—0
Fast Graph Kernel with Optical Random FeaturesCode1
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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