Principled approach to the selection of the embedding dimension of networks

Weiwei Gu, Aditya Tandon, Yong-Yeol Ahn and Filippo Radicchi ()
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Weiwei Gu: Beijing University of Chemical Technology
Aditya Tandon: Indiana University
Yong-Yeol Ahn: Indiana University
Filippo Radicchi: Indiana University

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Network embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension – small enough to be efficient and large enough to be effective – is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23795-5

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DOI: 10.1038/s41467-021-23795-5

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