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

Showing 721730 of 982 papers

TitleStatusHype
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints0
Graph Representation Learning for Infrared and Visible Image Fusion0
Graph Representation Learning for Interactive Biomolecule Systems0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing0
Graph Representation Learning for Popularity Prediction Problem: A Survey0
Graph Representation Learning for Spatial Image Steganalysis0
Graph representation learning for street networks0
Graph Representation Learning on Tissue-Specific Multi-Omics0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease0
Graph Representation Learning Towards Patents Network Analysis0
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Benchmark Results

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