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 451–500 of 927 papers

TitleStatusHype
DeepGCNs: Can GCNs Go as Deep as CNNs?Code0
Towards Sparse Hierarchical Graph ClassifiersCode0
Transformer and Snowball Graph Convolution Learning for Brain functional network ClassificationCode0
TreeRNN: Topology-Preserving Deep GraphEmbedding and LearningCode0
Graph Perceiver IO: A General Architecture for Graph Structured DataCode0
DOTIN: Dropping Task-Irrelevant Nodes for GNNsCode0
Understanding Attention and Generalization in Graph Neural NetworksCode0
Understanding Isomorphism Bias in Graph Data SetsCode0
Generalizing Downsampling from Regular Data to GraphsCode0
Capsule Graph Neural NetworkCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph ProximityCode0
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal TransportCode0
Universal Graph Transformer Self-Attention NetworksCode0
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural NetworksCode0
Verifying message-passing neural networks via topology-based bounds tighteningCode0
Graph Self-Supervised Learning with Learnable Structural and Positional EncodingsCode0
Wasserstein Graph Distance Based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman SubtreesCode0
Wasserstein Weisfeiler-Lehman Graph KernelsCode0
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean DatasetsCode0
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on GraphsCode0
Graph Star Net for Generalized Multi-Task LearningCode0
Generalized Equivariance and Preferential Labeling for GNN Node ClassificationCode0
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed GraphsCode0
Graph Style Transfer for Counterfactual ExplainabilityCode0
Weisfeiler and Leman Go Neural: Higher-order Graph Neural NetworksCode0
Edge Classification on Graphs: New Directions in Topological ImbalanceCode0
Graph Triple Attention Network: A Decoupled PerspectiveCode0
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis—0
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs—0
A Collective Learning Framework to Boost GNN Expressiveness—0
A Comparison of Graph Neural Networks for Malware Classification—0
A Differential Geometric View and Explainability of GNN on Evolving Graphs—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Graph Contrastive Learning with Cross-view Reconstruction—0
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
A graph similarity for deep learning—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
A Hierarchy of Graph Neural Networks Based on Learnable Local Features—0
A Hyperbolic-to-Hyperbolic Graph Convolutional Network—0
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification—0
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks—0
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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
8δ-2-LWLAccuracy85.5—Unverified
9DUGNNAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified