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

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Showing 301–350 of 927 papers

TitleStatusHype
PropEnc: A Property Encoder for Graph Neural Networks—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique GraphsCode0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
GSpect: Spectral Filtering for Cross-Scale Graph Classification—0
GSTAM: Efficient Graph Distillation with Structural Attention-MatchingCode0
Learning Tree-Structured Composition of Data AugmentationCode0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed GraphsCode0
Graph Classification with GNNs: Optimisation, Representation and Inductive BiasCode0
Graph Triple Attention Network: A Decoupled PerspectiveCode0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Knowledge Probing for Graph Representation Learning—0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques—0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—0
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation LearningCode0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks—0
Relaxing Graph Transformers for Adversarial Attacks—0
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach—0
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph ClassificationCode0
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNsCode0
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning—0
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural NetworksCode0
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph ClassificationCode0
Kolmogorov-Arnold Graph Neural Networks—0
TopoGCL: Topological Graph Contrastive LearningCode0
Next Level Message-Passing with Hierarchical Support GraphsCode0
Fast Tree-Field Integrators: From Low Displacement Rank to Topological TransformersCode0
Edge Classification on Graphs: New Directions in Topological ImbalanceCode0
On GNN explanability with activation rules—0
Robustness Inspired Graph Backdoor Defense—0
Motif-driven Subgraph Structure Learning for Graph ClassificationCode0
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters—0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge—0
A Canonicalization Perspective on Invariant and Equivariant LearningCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks—0
Towards Subgraph Isomorphism Counting with Graph Kernels—0
Hypergraph-enhanced Dual Semi-supervised Graph Classification—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