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 681–690 of 982 papers

TitleStatusHype
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
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Benchmark Results

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