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 421–430 of 982 papers

TitleStatusHype
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
Graph Representation learning for Audio & Music genre Classification—0
Differential Encoding for Improved Representation Learning over Graphs—0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
Graph Representation Learning for Interactive Biomolecule Systems—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
FMGNN: Fused Manifold Graph Neural Network—0
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Benchmark Results

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