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 561–570 of 982 papers

TitleStatusHype
A Comprehensive Survey on Deep Graph Representation Learning—0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Graph Representation Learning for Interactive Biomolecule Systems—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
FMGNN: Fused Manifold Graph Neural Network—0
A Survey on Malware Detection with Graph Representation Learning—0
Topological Pooling on GraphsCode0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Show:102550
← PrevPage 57 of 99Next →

Benchmark Results

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