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

Showing 751800 of 927 papers

TitleStatusHype
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
Improving Subgraph-GNNs via Edge-Level Ego-Network EncodingsCode0
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph KernelsCode0
Graph Neural Networks with Parallel Neighborhood Aggregations for Graph ClassificationCode0
Improving the interpretability of GNN predictions through conformal-based graph sparsificationCode0
Data-Driven Learning of Geometric Scattering NetworksCode0
Incorporating Heterophily into Graph Neural Networks for Graph ClassificationCode0
Reinforced Continual Learning for GraphsCode0
Supervised Community Detection with Line Graph Neural NetworksCode0
Relational Pooling for Graph RepresentationsCode0
Graph Neural Networks with convolutional ARMA filtersCode0
Graph Neural Networks Use Graphs When They Shouldn'tCode0
Verifying message-passing neural networks via topology-based bounds tighteningCode0
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured DataCode0
Relation order histograms as a network embedding toolCode0
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex ClusteringCode0
Understanding Attention and Generalization in Graph Neural NetworksCode0
A Novel Higher-order Weisfeiler-Lehman Graph ConvolutionCode0
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural NetworksCode0
Network Classification Based Structural Analysis of Real Networks and their Model-Generated CounterpartsCode0
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning ApproachCode0
Rep the Set: Neural Networks for Learning Set RepresentationsCode0
Residual Gated Graph ConvNetsCode0
IPC: A Benchmark Data Set for Learning with Graph-Structured DataCode0
Task-Agnostic Graph Neural Network Evaluation via Adversarial CollaborationCode0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNsCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Template based Graph Neural Network with Optimal Transport DistancesCode0
Rethinking the Item Order in Session-based Recommendation with Graph Neural NetworksCode0
Understanding Isomorphism Bias in Graph Data SetsCode0
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph ClassificationCode0
Graph Kernels Based on Linear Patterns: Theoretical and Experimental ComparisonsCode0
Tensor-Fused Multi-View Graph Contrastive LearningCode0
Kernel Graph Convolutional Neural NetworksCode0
Kernel method for persistence diagrams via kernel embedding and weight factorCode0
Tensor-view Topological Graph Neural NetworkCode0
k-hop Graph Neural NetworksCode0
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation LearningCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
DAGCN: Dual Attention Graph Convolutional NetworksCode0
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer LearningCode0
Graph isomorphism UNetCode0
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological SignaturesCode0
The GECo algorithm for Graph Neural Networks ExplanationCode0
An explainability framework for cortical surface-based deep learningCode0
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral EmbeddingCode0
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter TrendsCode0
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric SpaceCode0
A Canonicalization Perspective on Invariant and Equivariant LearningCode0
Show:102550
← PrevPage 16 of 19Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762Unverified
2HGP-SLAccuracy84.91Unverified
3rLap (unsupervised)Accuracy84.3Unverified
4TFGW ADJ (L=2)Accuracy82.9Unverified
5FIT-GNNAccuracy82.1Unverified
6DUGNNAccuracy81.7Unverified
7MEWISPoolAccuracy80.71Unverified
8CIN++Accuracy80.5Unverified
9MAGPoolAccuracy80.36Unverified
10SAEPoolAccuracy80.36Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100Unverified
2MEWISPoolAccuracy96.66Unverified
3TFGW ADJ (L=2)Accuracy96.4Unverified
4GIUNetAccuracy95.7Unverified
5G_InceptionAccuracy95Unverified
6GICAccuracy94.44Unverified
7CIN++Accuracy94.4Unverified
8sGINAccuracy94.14Unverified
9CANAccuracy94.1Unverified
10Deep WL SGN(0,1,2)Accuracy93.68Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1Unverified
2WKPI-kmeansAccuracy87.2Unverified
3FGW wl h=4 spAccuracy86.42Unverified
4WL-OA KernelAccuracy86.1Unverified
5WL-OAAccuracy86.1Unverified
6FGW wl h=2 spAccuracy85.82Unverified
7WWLAccuracy85.75Unverified
8DUGNNAccuracy85.5Unverified
9δ-2-LWLAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified