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

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Showing 251–300 of 927 papers

TitleStatusHype
Hypergraph Isomorphism ComputationCode2
How Curvature Enhance the Adaptation Power of Framelet GCNsCode0
Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification—0
A Survey on Graph Classification and Link Prediction based on GNN—0
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural NetworksCode0
An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics—0
Modeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite MatricesCode1
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph PoolingCode0
Structure-Aware Robustness Certificates for Graph ClassificationCode0
Globally Interpretable Graph Learning via Distribution Matching—0
Structure-Sensitive Graph Dictionary Embedding for Graph Classification—0
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph CoarseningCode0
Self-supervised Learning and Graph Classification under Heterophily—0
Analysis and Approximate Inference of Large Random Kronecker GraphsCode0
Explainable and Position-Aware Learning in Digital Pathology—0
Graph Mixup with Soft AlignmentsCode0
Path Neural Networks: Expressive and Accurate Graph Neural NetworksCode1
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
XInsight: Revealing Model Insights for GNNs with Flow-based Explanations—0
Graph Classification Gaussian Processes via Spectral Features—0
Randomized Schur Complement Views for Graph Contrastive LearningCode0
CIN++: Enhancing Topological Message PassingCode1
Learning Probabilistic Symmetrization for Architecture Agnostic EquivarianceCode1
Explaining and Adapting Graph Conditional Shift—0
Message-passing selection: Towards interpretable GNNs for graph classification—0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
Towards Semi-supervised Universal Graph Classification—0
Is Rewiring Actually Helpful in Graph Neural Networks?—0
Graph Inductive Biases in Transformers without Message PassingCode1
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Robust Ante-hoc Graph Explainer using Bilevel Optimization—0
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies—0
Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective—0
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding—0
Sequential Graph Neural Networks for Source Code Vulnerability Identification—0
DEGREE: Decomposition Based Explanation For Graph Neural NetworksCode0
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product NetworksCode2
DRew: Dynamically Rewired Message Passing with DelayCode1
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive LearningCode0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Deep Graph Reprogramming—0
TGNN: A Joint Semi-supervised Framework for Graph-level Classification—0
ID-MixGCL: Identity Mixup for Graph Contrastive Learning—0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPTCode2
Transformer and Snowball Graph Convolution Learning for Brain functional network ClassificationCode0
Topological Pooling on GraphsCode0
Structural Imbalance Aware Graph Augmentation Learning—0
A Comparison of Graph Neural Networks for Malware Classification—0
Exphormer: Sparse Transformers for GraphsCode1
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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