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 721–730 of 982 papers

TitleStatusHype
Data Considerations in Graph Representation Learning for Supply Chain Networks—0
Large-scale graph representation learning with very deep GNNs and self-supervision—0
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired DatasetCode0
Graph Representation Learning for Road Type ClassificationCode0
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal—0
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation—0
Multi-Level Graph Contrastive Learning—0
Multi-modal Graph Learning for Disease Prediction—0
Edge Representation Learning with HypergraphsCode1
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
Show:102550
← PrevPage 73 of 99Next →

Benchmark Results

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