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 891–900 of 982 papers

TitleStatusHype
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
Navigating the Dynamics of Financial Embeddings over Time—0
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative SamplingCode0
Quantifying Challenges in the Application of Graph Representation Learning—0
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
Graph Representation Learning Network via Adaptive SamplingCode0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks—0
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Benchmark Results

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