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 601–650 of 927 papers

TitleStatusHype
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters—0
Explainable and Position-Aware Learning in Digital Pathology—0
Explaining and Adapting Graph Conditional Shift—0
Exploiting Edge Features for Graph Neural Networks—0
Exploiting Edge Features in Graph Neural Networks—0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Extending local features with contextual information in graph kernels—0
Fairness Amidst Non-IID Graph Data: A Literature Review—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Flexible infinite-width graph convolutional networks and the importance of representation learning—0
FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
From Graph Diffusion to Graph Classification—0
From Graph Generation to Graph Classification—0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
Function Space Pooling For Graph Convolutional Networks—0
GABO: Graph Augmentations with Bi-level Optimization—0
GANExplainer: GAN-based Graph Neural Networks Explainer—0
Gaussian-Induced Convolution for Graphs—0
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation—0
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks—0
GDM: Dual Mixup for Graph Classification with Limited Supervision—0
Generalized Shortest Path Kernel on Graphs—0
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Geometrically Principled Connections in Graph Neural Networks—0
Geometric Random Walk Graph Neural Networks via Implicit Layers—0
Geometric Scattering for Graph Data Analysis—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features—0
G-Mixup: Graph Augmentation for Graph Classification—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
Going beyond persistent homology using persistent homology—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Gradient Inversion Attack on Graph Neural Networks—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Graph2Graph Learning with Conditional Autoregressive Models—0
Graph Adversarial Self-Supervised Learning—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction—0
Graph-Aware Transformer: Is Attention All Graphs Need?—0
Graph-based Argument Quality Assessment—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation—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
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified