SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 526–550 of 982 papers

TitleStatusHype
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Adversarial Classifier for Imbalanced Problems—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
Are Hyperbolic Representations in Graphs Created Equal?—0
A Scalable and Effective Alternative to Graph Transformers—0
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
A Self-supervised Mixed-curvature Graph Neural Network—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
A Survey on Graph Representation Learning Methods—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified