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 251–300 of 927 papers

TitleStatusHype
A Comparison of Graph Neural Networks for Malware Classification—0
Ego-based Entropy Measures for Structural Representations—0
Change Point Methods on a Sequence of Graphs—0
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection—0
Efficient graphlet kernels for large graph comparison—0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing—0
Graph-Graph Similarity Network—0
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks—0
Graph embedding using multi-layer adjacent point merging model—0
Edge Contraction Pooling for Graph Neural Networks—0
PropEnc: A Property Encoder for Graph Neural Networks—0
Graph Domain Adaptation: A Generative View—0
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network—0
Edge but not Least: Cross-View Graph Pooling—0
Dynamics Based Features For Graph Classification—0
A Collective Learning Framework to Boost GNN Expressiveness—0
Graph data augmentation with Gromow-Wasserstein Barycenters—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations—0
Graph Convolutional Neural Networks via Motif-based Attention—0
Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification—0
Capsule Graph Neural Networks with EM Routing—0
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing—0
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks—0
Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos—0
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs—0
Domain Adaptive Graph Classification—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Cached Operator Reordering: A Unified View for Fast GNN Training—0
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Graph Contrastive Learning with Cross-view Reconstruction—0
Distribution Preserving Graph Representation Learning—0
Distinguishing Enzyme Structures from Non-enzymes Without Alignments—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Graph Convolutional Neural Networks based on Quantum Vertex Saliency—0
GraphCrop: Subgraph Cropping for Graph Classification—0
Graph Classification via Discriminative Edge Feature Learning—0
Bridging Graph Network to Lifelong Learning with Feature Interaction—0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal—0
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
Graph Classification with 2D Convolutional Neural Networks—0
Discriminative structural graph classification—0
Discriminative Graph Autoencoder—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