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 801–850 of 927 papers

TitleStatusHype
Neural Pooling for Graph Neural Networks—0
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection—0
Customized Graph Neural Networks—0
Improving Graph Neural Networks with Learnable Propagation Operators—0
One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion—0
One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting—0
On GNN explanability with activation rules—0
On Graph Classification Networks, Datasets and Baselines—0
On Single-environment Extrapolations in Graph Classification and Regression Tasks—0
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
On the Expressive Power of Graph Neural Networks—0
On the Expressivity of Persistent Homology in Graph Learning—0
On Valid Optimal Assignment Kernels and Applications to Graph Classification—0
Optimisation of Spectral Wavelets for Persistence-based Graph Classification—0
Ordinary differential equations on graph networks—0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks—0
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis—0
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing—0
Planning by case-based reasoning based on fuzzy logic—0
Polynomial Graph Convolutional Networks—0
Pooling in Graph Convolutional Neural Networks—0
Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification—0
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification—0
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies—0
Quantum-based subgraph convolutional neural networks—0
Quantum Graph Convolutional Neural Networks—0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs—0
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification—0
Random Walk Graph Neural Networks—0
Recovering Missing Node Features with Local Structure-based Embeddings—0
Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks—0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
Relation Regularized Scene Graph Generation—0
Relaxing Graph Transformers for Adversarial Attacks—0
RetGK: Graph Kernels based on Return Probabilities of Random Walks—0
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective—0
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification—0
Revisiting Graph Neural Networks for Link Prediction—0
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements—0
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction—0
Rhomboid Tiling for Geometric Graph Deep Learning—0
Ring Reservoir Neural Networks for Graphs—0
Robust Ante-hoc Graph Explainer using Bilevel Optimization—0
Robust Hierarchical Graph Classification with Subgraph Attention—0
Robustness Inspired Graph Backdoor Defense—0
Robustness of Graph Classification: failure modes, causes, and noise-resistant loss in Graph Neural Networks—0
Saliency-Aware Regularized Graph Neural Network—0
Sampling and Recovery of Graph Signals based on 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
8δ-2-LWLAccuracy85.5—Unverified
9DUGNNAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified