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 41–50 of 982 papers

TitleStatusHype
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
A Gentle Introduction to Deep Learning for GraphsCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
A Large-Scale Database for Graph Representation LearningCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
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Benchmark Results

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