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 701–750 of 927 papers

TitleStatusHype
A graph similarity for deep learning—0
Random Walk Graph Neural Networks—0
COPT: Coordinated Optimal Transport on Graphs—0
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
Sampling and Recovery of Graph Signals based on Graph Neural Networks—0
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer—0
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents—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
Co-embedding of Nodes and Edges with Graph Neural Networks—0
Topology-Aware Graph Pooling Networks—0
K-plex Cover Pooling for Graph Neural Networks—0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
Towards Expressive Graph RepresentationCode0
Understanding the Power of Persistence Pairing via Permutation Test—0
Data-Driven Learning of Geometric Scattering NetworksCode0
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks—0
Revisiting Graph Neural Networks for Link Prediction—0
GraphCrop: Subgraph Cropping for Graph Classification—0
Contrastive Self-supervised Learning for Graph Classification—0
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing—0
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization—0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
PiNet: Attention Pooling for Graph ClassificationCode0
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription—0
Multi-view adaptive graph convolutions for graph classification—0
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning—0
Robust Hierarchical Graph Classification with Subgraph Attention—0
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification—0
A Novel Higher-order Weisfeiler-Lehman Graph ConvolutionCode0
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification—0
TreeRNN: Topology-Preserving Deep GraphEmbedding and LearningCode0
Graph Pooling with Node Proximity for Hierarchical Representation Learning—0
Graph-Aware Transformer: Is Attention All Graphs Need?—0
Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization—0
Little Ball of Fur: A Python Library for Graph Sampling—0
SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Customized Graph Neural Networks—0
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection—0
How hard is to distinguish graphs with graph neural networks?—0
Ring Reservoir Neural Networks for Graphs—0
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network—0
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Pooling in Graph Convolutional Neural Networks—0
Geometrically Principled Connections in Graph 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