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 501–510 of 982 papers

TitleStatusHype
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph EmbeddingsCode2
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
Material Prediction for Design Automation Using Graph Representation LearningCode0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
Periodic Graph Transformers for Crystal Material Property PredictionCode1
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
Revisiting Embeddings for Graph Neural Networks—0
Cell Attention NetworksCode0
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Benchmark Results

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