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 301350 of 927 papers

TitleStatusHype
Learning Tree-Structured Composition of Data AugmentationCode0
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter TrendsCode0
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric SpaceCode0
Learning Deep Graph Representations via Convolutional Neural NetworksCode0
An End-to-End Deep Learning Architecture for Graph ClassificationCode0
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral EmbeddingCode0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
k-hop Graph Neural NetworksCode0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
Kernel method for persistence diagrams via kernel embedding and weight factorCode0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Learning Universal Adversarial Perturbations with Generative ModelsCode0
IPC: A Benchmark Data Set for Learning with Graph-Structured DataCode0
Graph Capsule Convolutional Neural NetworksCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured DataCode0
Debiasing Graph Neural Networks via Learning Disentangled Causal SubstructureCode0
DDGK: Learning Graph Representations for Deep Divergence Graph KernelsCode0
Improving the interpretability of GNN predictions through conformal-based graph sparsificationCode0
GraphAttacker: A General Multi-Task GraphAttack FrameworkCode0
GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learningCode0
Improving Subgraph-GNNs via Edge-Level Ego-Network EncodingsCode0
Incorporating Heterophily into Graph Neural Networks for Graph ClassificationCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
GraKeL: A Graph Kernel Library in PythonCode0
Network Classification Based Structural Analysis of Real Networks and their Model-Generated CounterpartsCode0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
Analysis and Approximate Inference of Large Random Kronecker GraphsCode0
ImageNet Classification with Deep Convolutional Neural NetworksCode0
Identity Inference on Blockchain using Graph Neural NetworkCode0
Data-Driven Learning of Geometric Scattering NetworksCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning ApproachCode0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram IntersectionCode0
DAGCN: Dual Attention Graph Convolutional NetworksCode0
Curvature-based Pooling within Graph Neural NetworksCode0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On GraphsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Kernel Graph Convolutional Neural NetworksCode0
Global Weisfeiler-Lehman Graph KernelsCode0
Cross-Domain Few-Shot Graph ClassificationCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation PoolingCode0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical GuaranteesCode0
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural NetworksCode0
Counterfactual Explanations for Graph Classification Through the Lenses of DensityCode0
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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
8δ-2-LWLAccuracy85.5Unverified
9DUGNNAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified