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 851–900 of 927 papers

TitleStatusHype
Sanity Check for Persistence Diagrams—0
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding—0
Scaling Up Graph Homomorphism Features with Efficient Data Structures—0
Scaling up graph homomorphism for classification via sampling—0
Schreier-Coset Graph Propagation—0
Self-supervised Learning and Graph Classification under Heterophily—0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification—0
Semi-Supervised Hierarchical Graph Classification—0
Sequential Graph Neural Networks for Source Code Vulnerability Identification—0
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning—0
Shift Aggregate Extract Networks—0
Should Graph Neural Networks Use Features, Edges, Or Both?—0
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training—0
SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features—0
Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective—0
Sliced Wasserstein Kernel for Persistence Diagrams—0
SoftEdge: Regularizing Graph Classification with Random Soft Edges—0
Solving graph compression via optimal transport—0
Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN—0
Spatial-temporal associations representation and application for process monitoring using graph convolution neural network—0
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling—0
SPGP: Structure Prototype Guided Graph Pooling—0
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network—0
SSHPool: The Separated Subgraph-based Hierarchical Pooling—0
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs—0
Structural Explanations for Graph Neural Networks using HSIC—0
Structural Imbalance Aware Graph Augmentation Learning—0
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification—0
Structure Representation Learning by Jointly Learning to Pool and Represent—0
Structure-Sensitive Graph Dictionary Embedding for Graph Classification—0
Subgraph Attention for Node Classification and Hierarchical Graph Pooling—0
Subgraph Networks with Application to Structural Feature Space Expansion—0
Supervised Contrastive Learning with Structure Inference for Graph Classification—0
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach—0
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose—0
Temporal Graph Kernels for Classifying Dissemination Processes—0
Test-Time Training for Graph Neural Networks—0
Text Categorization as a Graph Classification Problem—0
TGNN: A Joint Semi-supervised Framework for Graph-level Classification—0
The Infinite Contextual Graph Markov Model—0
The Multiscale Laplacian Graph Kernel—0
Fundamental Limits of Deep Graph Convolutional Networks—0
Topology-Aware Graph Pooling Networks—0
Topology-Aware Pooling via Graph Attention—0
Topology Based Scalable Graph Kernels—0
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations—0
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection—0
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks—0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts—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