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 531–540 of 982 papers

TitleStatusHype
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
Show:102550
← PrevPage 54 of 99Next →

Benchmark Results

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