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

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Showing 361–370 of 982 papers

TitleStatusHype
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
GraLSP: Graph Neural Networks with Local Structural Patterns—0
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
Graph AI in Medicine—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
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Benchmark Results

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