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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–700 of 927 papers

TitleStatusHype
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms—0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
Structure-Aware Hierarchical Graph Pooling using Information BottleneckCode0
Identity Inference on Blockchain using Graph Neural NetworkCode0
Quadratic GCN for Graph ClassificationCode0
A Hyperbolic-to-Hyperbolic Graph Convolutional Network—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Smart Vectorizations for Single and Multiparameter PersistenceCode0
Scaling up graph homomorphism for classification via sampling—0
GABO: Graph Augmentations with Bi-level Optimization—0
Unified Graph Structured Models for Video Understanding—0
Graph Classification by Mixture of Diverse Experts—0
GraphDIVE: Graph Classification by Mixture of Diverse ExpertsCode0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Should Graph Neural Networks Use Features, Edges, Or Both?—0
Scaling Up Graph Homomorphism Features with Efficient Data Structures—0
Sanity Check for Persistence Diagrams—0
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Generalized Equivariance and Preferential Labeling for GNN Node ClassificationCode0
Ego-based Entropy Measures for Structural Representations on Graphs—0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Learning Graph Representations—0
Graph Classification Based on Skeleton and Component Features—0
Learning Parametrised Graph Shift OperatorsCode0
GraphAttacker: A General Multi-Task GraphAttack FrameworkCode0
Optimisation of Spectral Wavelets for Persistence-based Graph Classification—0
Polynomial Graph Convolutional Networks—0
Bridging Graph Network to Lifelong Learning with Feature Interaction—0
Graph-Graph Similarity Network—0
Graph Pooling by Edge Cut—0
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement—0
Graph Structural Aggregation for Explainable Learning—0
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling—0
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning—0
Neural Pooling for Graph Neural Networks—0
One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting—0
On Single-environment Extrapolations in Graph Classification and Regression Tasks—0
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations—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
An Experimental Study of the Transferability of Spectral Graph NetworksCode0
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
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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