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 881–890 of 982 papers

TitleStatusHype
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender SystemsCode1
Understanding Negative Sampling in Graph Representation LearningCode1
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks—0
Machine Learning on Graphs: A Model and Comprehensive TaxonomyCode1
Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain GraphCode0
SIGN: Scalable Inception Graph Neural NetworksCode1
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Graph Representation Learning via Ladder Gamma Variational AutoencodersCode0
Gossip and Attend: Context-Sensitive Graph Representation LearningCode0
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Benchmark Results

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