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 701–750 of 927 papers

TitleStatusHype
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing—0
Graph Structural Aggregation for Explainable Learning—0
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Graph Transformer—0
Graph Transformers without Positional Encodings—0
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation—0
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations—0
GSpect: Spectral Filtering for Cross-Scale Graph Classification—0
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks—0
HaarPooling: Graph Pooling with Compressive Haar Basis—0
Halting in Random Walk Kernels—0
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
High-Order Pooling for Graph Neural Networks with Tensor Decomposition—0
How can we generalise learning distributed representations of graphs?—0
How hard is to distinguish graphs with graph neural networks?—0
Hypergraph-enhanced Dual Semi-supervised Graph Classification—0
ID-MixGCL: Identity Mixup for Graph Contrastive Learning—0
Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Independence Promoted Graph Disentangled Networks—0
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective—0
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Inferential SIR-GN: Scalable Graph Representation Learning—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Globally Interpretable Graph Learning via Distribution Matching—0
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps—0
Intrusion-Free Graph Mixup—0
ifMixup: Interpolating Graph Pair to Regularize Graph Classification—0
Invariant embedding for graph classification—0
Investigating Transfer Learning in Graph Neural Networks—0
iPool -- Information-based Pooling in Hierarchical Graph Neural Networks—0
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
Is Rewiring Actually Helpful in Graph Neural Networks?—0
IV-GNN : Interval Valued Data Handling Using Graph Neural Network—0
Joint Reconstruction and Parcellation of Cortical Surfaces—0
Kernel Graph Convolutional Neural Nets—0
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification—0
Knowledge Probing for Graph Representation Learning—0
Kolmogorov-Arnold Graph Neural Networks—0
K-plex Cover Pooling for Graph Neural Networks—0
Label-Aware Graph Convolutional Networks—0
Labeled Graph Generative Adversarial Networks—0
Label-invariant Augmentation for 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