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 551–600 of 927 papers

TitleStatusHype
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
Contrastive Learning with Negative Sampling Correction—0
Contrastive Self-supervised Learning for Graph Classification—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
COPT: Coordinated Optimal Transport on Graphs—0
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning—0
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription—0
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer—0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
DEEP GEOMETRICAL GRAPH CLASSIFICATION—0
Deep Graph Attention Model—0
Deep Graph Kernels—0
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network—0
Deep Graph Reprogramming—0
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning—0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability—0
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
Degree-Conscious Spiking Graph for Cross-Domain Adaptation—0
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model—0
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph—0
Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation—0
Diffusing Graph Attention—0
Discriminative Graph Autoencoder—0
Discriminative structural graph classification—0
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal—0
Distinguishing Enzyme Structures from Non-enzymes Without Alignments—0
Distribution Preserving Graph Representation Learning—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Domain Adaptive Graph Classification—0
DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing—0
Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification—0
Dynamics Based Features For Graph Classification—0
Edge but not Least: Cross-View Graph Pooling—0
Edge Contraction Pooling for Graph Neural Networks—0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
Efficient graphlet kernels for large graph comparison—0
Ego-based Entropy Measures for Structural Representations—0
Ego-based Entropy Measures for Structural Representations on Graphs—0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks—0
Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques—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