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 411–420 of 982 papers

TitleStatusHype
A Survey on Graph Representation Learning Methods—0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
Graph Ordering: Towards the Optimal by Learning—0
Graph Partial Label Learning with Potential Cause Discovering—0
Graph Persistence goes Spectral—0
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements—0
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks—0
3D Hand Pose Estimation via Regularized Graph Representation Learning—0
FMGNN: Fused Manifold Graph Neural Network—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Show:102550
← PrevPage 42 of 99Next →

Benchmark Results

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