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 631–640 of 982 papers

TitleStatusHype
Uplifting Message Passing Neural Network with Graph Original Information—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
Material Prediction for Design Automation Using Graph Representation LearningCode0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Show:102550
← PrevPage 64 of 99Next →

Benchmark Results

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