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 81–90 of 982 papers

TitleStatusHype
CCGL: Contrastive Cascade Graph LearningCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
A Representation Learning Framework for Property GraphsCode1
A Gentle Introduction to Deep Learning for GraphsCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
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Benchmark Results

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