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 551–600 of 927 papers

TitleStatusHype
Relation order histograms as a network embedding toolCode0
Do Transformers Really Perform Bad for Graph Representation?Code2
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernelsCode0
Graph2Graph Learning with Conditional Autoregressive Models—0
Convergent Graph SolversCode1
Mixup for Node and Graph ClassificationCode1
How Attentive are Graph Attention Networks?Code1
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms—0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
Structure-Aware Hierarchical Graph Pooling using Information BottleneckCode0
User Preference-aware Fake News DetectionCode1
Permutation-Invariant Variational Autoencoder for Graph-Level Representation LearningCode1
Quadratic GCN for Graph ClassificationCode0
Identity Inference on Blockchain using Graph Neural NetworkCode0
A Hyperbolic-to-Hyperbolic Graph Convolutional Network—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Smart Vectorizations for Single and Multiparameter PersistenceCode0
Scaling up graph homomorphism for classification via sampling—0
GABO: Graph Augmentations with Bi-level Optimization—0
Parameterized Hypercomplex Graph Neural Networks for Graph ClassificationCode1
Unified Graph Structured Models for Video Understanding—0
Graph Classification by Mixture of Diverse Experts—0
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
GraphDIVE: Graph Classification by Mixture of Diverse ExpertsCode0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Should Graph Neural Networks Use Features, Edges, Or Both?—0
Scaling Up Graph Homomorphism Features with Efficient Data Structures—0
Sanity Check for Persistence Diagrams—0
Size-Invariant Graph Representations for Graph Classification ExtrapolationsCode1
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
Graph Autoencoder for Graph Compression and Representation LearningCode1
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities—0
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Generalized Equivariance and Preferential Labeling for GNN Node ClassificationCode0
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Ego-based Entropy Measures for Structural Representations on Graphs—0
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message PassingCode1
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Online Graph Dictionary LearningCode1
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Enhance Information Propagation for Graph Neural Network by Heterogeneous AggregationsCode1
Learning Graph Representations—0
Graph Classification Based on Skeleton and Component Features—0
[Re] Parameterized Explainer for Graph Neural NetworkCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Identity-aware Graph Neural NetworksCode2
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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
9SAEPoolAccuracy80.36—Unverified
10MAGPoolAccuracy80.36—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-OAAccuracy86.1—Unverified
5WL-OA KernelAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified