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 101150 of 927 papers

TitleStatusHype
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
HL-Net: Heterophily Learning Network for Scene Graph GenerationCode1
FAITH: Few-Shot Graph Classification with Hierarchical Task GraphsCode1
COPT: Coordinated Optimal Transport for Graph SketchingCode1
Imbalanced Graph Classification via Graph-of-Graph Neural NetworksCode1
Improving Graph Neural Network Expressivity via Subgraph Isomorphism CountingCode1
Fake News Detection on Social Media using Geometric Deep LearningCode1
Improving the Effective Receptive Field of Message-Passing Neural NetworksCode1
Approximate Network Motif Mining Via Graph LearningCode1
Inference Attacks Against Graph Neural NetworksCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Learning distributed representations of graphs with Geo2DRCode1
Learning Graph Normalization for Graph Neural NetworksCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological AttacksCode1
Differentially Private Graph Classification with GNNsCode1
LightGCN: Simplifying and Powering Graph Convolution Network for RecommendationCode1
Exphormer: Sparse Transformers for GraphsCode1
Agent-based Graph Neural NetworksCode1
CIN++: Enhancing Topological Message PassingCode1
CKGConv: General Graph Convolution with Continuous KernelsCode1
Directional Graph NetworksCode1
Membership Inference Attack on Graph Neural NetworksCode1
Metric Based Few-Shot Graph ClassificationCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and ImplicationsCode1
Enhance Information Propagation for Graph Neural Network by Heterogeneous AggregationsCode1
Fast Graph Kernel with Optical Random FeaturesCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
Edge Representation Learning with HypergraphsCode1
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and GeneralizabilityCode1
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural NetworksCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Total Variation Graph Neural NetworksCode1
Energy TransformerCode1
Evaluating Modules in Graph Contrastive LearningCode1
Expander Graph PropagationCode1
Composition-based Multi-Relational Graph Convolutional NetworksCode1
Compressing Deep Graph Neural Networks via Adversarial Knowledge DistillationCode1
Exploring Fake News Detection with Heterogeneous Social Media Context GraphsCode1
Factorizable Graph Convolutional NetworksCode1
Federated Graph Classification over Non-IID GraphsCode1
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer ProxiesCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Contrastive Multi-View Representation Learning on GraphsCode1
Gated Graph Sequence Neural NetworksCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Convergent Graph SolversCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
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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
8DUGNNAccuracy85.5Unverified
9δ-2-LWLAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified