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 201–250 of 927 papers

TitleStatusHype
Improving Subgraph-GNNs via Edge-Level Ego-Network EncodingsCode0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
On the Initialization of Graph Neural NetworksCode0
Recurrent Distance Filtering for Graph Representation LearningCode1
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal—0
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning—0
Hard Label Black Box Node Injection Attack on Graph Neural NetworksCode0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps—0
Going beyond persistent homology using persistent homology—0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral EmbeddingCode0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
BLIS-Net: Classifying and Analyzing Signals on Graphs—0
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Mirage: Model-Agnostic Graph Distillation for Graph ClassificationCode0
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings AugmentationCode0
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose—0
One for All: Towards Training One Graph Model for All Classification TasksCode2
Graph-level Representation Learning with Joint-Embedding Predictive ArchitecturesCode1
GDM: Dual Mixup for Graph Classification with Limited Supervision—0
Recovering Missing Node Features with Local Structure-based Embeddings—0
Graph Neural Networks Use Graphs When They Shouldn'tCode0
Filtration Surfaces for Dynamic Graph ClassificationCode0
Generalized Simplicial Attention Neural NetworksCode0
MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention—0
Where Did the Gap Go? Reassessing the Long-Range Graph BenchmarkCode1
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR ImagesCode0
Curvature-based Pooling within Graph Neural NetworksCode0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts—0
Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN—0
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Universal Graph Continual Learning—0
Graph isomorphism UNetCode0
Cached Operator Reordering: A Unified View for Fast GNN Training—0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
Enhancing Graph Transformers with Hierarchical Distance Structural EncodingCode1
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based SimilarityCode1
Modeling Edge Features with Deep Bayesian Graph NetworksCode0
S-Mixup: Structural Mixup for Graph Neural NetworksCode1
Graph Out-of-Distribution Generalization with Controllable Data Augmentation—0
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes—0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification—0
Evaluating Link Prediction Explanations for Graph Neural NetworksCode0
Counterfactual Explanations for Graph Classification Through the Lenses of DensityCode0
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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