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 811–820 of 982 papers

TitleStatusHype
Pair-view Unsupervised Graph Representation Learning—0
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning—0
Graph Mixture Density NetworksCode1
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base—0
GL-Coarsener: A Graph representation learning framework to construct coarse grid hierarchy for AMG solversCode0
Node Similarity Preserving Graph Convolutional NetworksCode1
A Large-Scale Database for Graph Representation LearningCode1
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
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Benchmark Results

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