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 641–650 of 982 papers

TitleStatusHype
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Semantic Communication Enhanced by Knowledge Graph Representation Learning—0
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees—0
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease—0
SGA: A Graph Augmentation Method for Signed Graph Neural Networks—0
SGR: Self-Supervised Spectral Graph Representation Learning—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
Siamese Attribute-missing Graph Auto-encoder—0
Show:102550
← PrevPage 65 of 99Next →

Benchmark Results

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