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 601–650 of 927 papers

TitleStatusHype
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters—0
Multi network InfoMax: A pre-training method involving graph convolutional networks—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks—0
Watermarking Graph Neural Networks based on Backdoor Attacks—0
Capsule Graph Neural Networks with EM Routing—0
Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos—0
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs—0
ifMixup: Interpolating Graph Pair to Regularize Graph Classification—0
SoGCN: Second-Order Graph Convolutional NetworksCode0
Fast Attributed Graph Embedding via Density of StatesCode0
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
Permute Me Softly: Learning Soft Permutations for Graph RepresentationsCode0
G-Mixup: Graph Augmentation for Graph Classification—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Metric Learning on Temporal Graphs via Few-Shot Examples—0
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs—0
The Infinite Contextual Graph Markov Model—0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation—0
Intrusion-Free Graph Mixup—0
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction—0
Wasserstein Weisfeiler-Lehman Subtree Distance for Graph-Structured Data—0
Geometric Random Walk Graph Neural Networks via Implicit Layers—0
Edge but not Least: Cross-View Graph Pooling—0
Structural Optimization Makes Graph Classification Simpler and BetterCode0
Graph-based Argument Quality Assessment—0
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
Blockchain Phishing Scam Detection via Multi-channel Graph Classification—0
Natural Numerical Networks for Natura 2000 habitats classification by satellite images—0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Compensation Learning—0
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks—0
EGC2: Enhanced Graph Classification with Easy Graph CompressionCode0
Automated Graph Learning via Population Based Self-Tuning GCN—0
Quantum Graph Convolutional Neural Networks—0
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural NetworksCode0
Message Passing in Graph Convolution Networks via Adaptive Filter Banks—0
Attacking Graph Classification via Bayesian Optimisation—0
On the approximation capability of GNNs in node classification/regression tasksCode0
Graph Domain Adaptation: A Generative View—0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Relation order histograms as a network embedding toolCode0
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernelsCode0
Graph2Graph Learning with Conditional Autoregressive Models—0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
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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