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arXiv · 1910.05534

Spectral embedding of weighted graphs

Ian Gallagher·Andrew Jones·Anna Bertiger·Carey Priebe·Patrick Rubin-Delanchy
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Abstract

When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results. To formalize this idea, we consider the asymptotic behavior of spectral embedding for different edge-weight representations, under a generic low rank model. We measure the quality of different embeddings -- which can be on entirely different scales -- by how easy it is to distinguish communities, in an information-theoretic sense. For common types of weighted graphs, such as count networks or p-value networks, we find that transformations such as tempering or thresholding can be highly beneficial, both in theory and in practice.

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Ian Gallagher, Andrew Jones, Anna Bertiger, Carey Priebe, Patrick Rubin-Delanchy. 2019-10-12. Spectral embedding of weighted graphs. https://arxiv.org/abs/1910.05534

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