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 101–150 of 927 papers

TitleStatusHype
Imbalanced Graph Classification via Graph-of-Graph Neural NetworksCode1
Improving Graph Neural Network Expressivity via Subgraph Isomorphism CountingCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Inductive Representation Learning on Large GraphsCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information MaximizationCode1
Graph Attention NetworksCode1
Expander Graph PropagationCode1
Approximate Network Motif Mining Via Graph LearningCode1
Exphormer: Sparse Transformers for GraphsCode1
GOT: An Optimal Transport framework for Graph comparisonCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer ProxiesCode1
Learning Probabilistic Symmetrization for Architecture Agnostic EquivarianceCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological AttacksCode1
FAITH: Few-Shot Graph Classification with Hierarchical Task GraphsCode1
Line Graph Neural Networks for Link PredictionCode1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
Agent-based Graph Neural NetworksCode1
CIN++: Enhancing Topological Message PassingCode1
CKGConv: General Graph Convolution with Continuous KernelsCode1
Masked Relation Learning for DeepFake DetectionCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Memory-Based Graph NetworksCode1
Metric Based Few-Shot Graph ClassificationCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
Graph Cross Networks with Vertex Infomax PoolingCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Gated Graph Sequence Neural NetworksCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and GeneralizabilityCode1
COPT: Coordinated Optimal Transport for Graph SketchingCode1
Global Counterfactual Explainer for Graph Neural NetworksCode1
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and ImplicationsCode1
GNNExplainer: Generating Explanations for Graph Neural NetworksCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
graph2vec: Learning Distributed Representations of GraphsCode1
Composition-based Multi-Relational Graph Convolutional NetworksCode1
Compressing Deep Graph Neural Networks via Adversarial Knowledge DistillationCode1
Causal Attention for Interpretable and Generalizable Graph ClassificationCode1
Graph BackdoorCode1
Total Variation Graph Neural NetworksCode1
Discovering Invariant Rationales for Graph Neural NetworksCode1
Graph Inductive Biases in Transformers without Message PassingCode1
Contrastive Multi-View Representation Learning on GraphsCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
Differentially Private Graph Classification with GNNsCode1
Convergent Graph SolversCode1
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal TransportCode1
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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
9SAEPoolAccuracy80.36—Unverified
10MAGPoolAccuracy80.36—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-OAAccuracy86.1—Unverified
5WL-OA KernelAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified