Abstract
We study random graphs with latent geometric structure, where the probability of each edge depends on the underlying random positions corresponding to the two endpoints. We consider the setting where this conditional probability is a general monotone increasing function of the inner product of two vectors; such a function can naturally be viewed as the cumulative distribution function of some independent random variable. A one-parameter family of random graphs, characterized by the variance of this random variable, that smoothly interpolates between a random dot product graph and an Erdős–Rényi random graph, is investigated. Focusing on the dense regime, we prove phase transitions of detecting geometry in these graphs, in terms of the dimension of the underlying geometric space and the variance parameter: When the dimension is high or the variance is large, the graph is similar to an Erdős–Rényi graph with the same edge density; in other parameter regimes, there is a computationally efficient signed triangle statistic that can distinguish them. The proofs make use of information-theoretic inequalities and concentration of measure phenomena.
Funding Statement
The authors were supported in part by NSF grant DMS-1811724.
Acknowledgments
The authors would like to thank Ramon van Handel for insightful comments on several results and Jiacheng Zhang for suggesting the proofs of Lemma 2.6 and Lemma 4.3. We also would like to thank the editor and anonymous reviewers for refereeing the article and their helpful comments.
Citation
Suqi Liu. Miklós Z. Rácz. "A probabilistic view of latent space graphs and phase transitions." Bernoulli 29 (3) 2417 - 2441, August 2023. https://doi.org/10.3150/22-BEJ1547
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