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 351–400 of 927 papers

TitleStatusHype
Hypergraph-enhanced Dual Semi-supervised Graph Classification—0
Conditional Local Feature Encoding for Graph Neural Networks—0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Transductive Spiking Graph Neural Networks for Loihi—0
One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion—0
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
Improving the interpretability of GNN predictions through conformal-based graph sparsificationCode0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
Graph data augmentation with Gromow-Wasserstein Barycenters—0
Harnessing the Power of Large Language Model for Uncertainty Aware Graph ProcessingCode0
Learning the mechanisms of network growthCode0
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification—0
SSHPool: The Separated Subgraph-based Hierarchical Pooling—0
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks—0
Molecular Classification Using Hyperdimensional Graph Classification—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
A Differential Geometric View and Explainability of GNN on Evolving Graphs—0
Cooperative Classification and Rationalization for Graph GeneralizationCode0
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning—0
Multi-Scale Subgraph Contrastive Learning—0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Verifying message-passing neural networks via topology-based bounds tighteningCode0
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective—0
Class-Balanced and Reinforced Active Learning on Graphs—0
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model—0
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning—0
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute—0
Generalization Error of Graph Neural Networks in the Mean-field RegimeCode0
Flexible infinite-width graph convolutional networks and the importance of representation learning—0
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelsCode0
Towards Neural Scaling Laws on GraphsCode0
Graph Transformers without Positional Encodings—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks—0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
Tensor-view Topological Graph Neural NetworkCode0
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph ClassificationCode0
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram IntersectionCode0
Contrastive Learning with Negative Sampling Correction—0
On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social NetworksCode0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
On the Expressive Power of Graph Neural Networks—0
On Discprecncies between Perturbation Evaluations of Graph Neural Network AttributionsCode0
Saliency-Aware Regularized Graph Neural Network—0
Domain Adaptive Graph Classification—0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
LightGCN: Evaluated and EnhancedCode0
Improving Subgraph-GNNs via Edge-Level Ego-Network EncodingsCode0
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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