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 541–550 of 982 papers

TitleStatusHype
Are Hyperbolic Representations in Graphs Created Equal?—0
A Scalable and Effective Alternative to Graph Transformers—0
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
A Self-supervised Mixed-curvature Graph Neural Network—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
A Survey on Graph Representation Learning Methods—0
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Benchmark Results

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