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 141–150 of 982 papers

TitleStatusHype
Certifiably Robust Graph Contrastive LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Graph External Attention Enhanced TransformerCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
A step towards neural genome assemblyCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
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Benchmark Results

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