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 351400 of 927 papers

TitleStatusHype
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language ProcessingCode0
Expander Graph PropagationCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Graph Classification via Discriminative Edge Feature Learning0
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural NetworksCode1
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges0
Debiasing Graph Neural Networks via Learning Disentangled Causal SubstructureCode0
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks0
Joint Reconstruction and Parcellation of Cortical Surfaces0
SPGP: Structure Prototype Guided Graph Pooling0
Cell Attention NetworksCode0
Graph Contrastive Learning with Cross-view Reconstruction0
Explainability in subgraphs-enhanced Graph Neural NetworksCode0
Graph Perceiver IO: A General Architecture for Graph Structured DataCode0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability0
Reinforced Continual Learning for GraphsCode0
Higher-order Clustering and Pooling for Graph Neural NetworksCode0
A Class-Aware Representation Refinement Framework for Graph Classification0
The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph ClassificationCode0
Second-Order Global Attention Networks for Graph Classification and RegressionCode0
Learnable Filters for Geometric Scattering Modules0
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification0
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference0
Generalizing Downsampling from Regular Data to GraphsCode0
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations0
Maximal Independent Vertex Set applied to Graph Pooling0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks0
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural NetworksCode1
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture SearchCode1
Wasserstein Graph Distance Based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman SubtreesCode0
Pure Transformers are Powerful Graph LearnersCode1
TREE-G: Decision Trees Contesting Graph Neural NetworksCode1
Transforming PageRank into an Infinite-Depth Graph Neural NetworkCode1
BAGEL: A Benchmark for Assessing Graph Neural Network ExplanationsCode1
Structural Entropy Guided Graph Hierarchical PoolingCode1
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training0
Agent-based Graph Neural NetworksCode1
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective0
0/1 Deep Neural Networks via Block Coordinate Descent0
Boosting Graph Structure Learning with Dummy NodesCode1
Long Range Graph BenchmarkCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
Semi-Supervised Hierarchical Graph Classification0
Fundamental Limits in Formal Verification of Message-Passing Neural Networks0
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link PredictionCode1
Metric Based Few-Shot Graph ClassificationCode1
A Simple yet Effective Method for Graph ClassificationCode0
Approximate Network Motif Mining Via Graph LearningCode1
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph ClassificationCode0
Shortest Path Networks for Graph Property PredictionCode1
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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