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 591–600 of 982 papers

TitleStatusHype
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Understanding microbiome dynamics via interpretable graph representation learningCode0
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
Distribution Preserving Graph Representation Learning—0
Sign and Basis Invariant Networks for Spectral Graph Representation LearningCode1
Message passing all the way up—0
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning—0
A Survey of Pretraining on Graphs: Taxonomy, Methods, and ApplicationsCode2
Adversarial Graph Contrastive Learning with Information RegularizationCode0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
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Benchmark Results

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