Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 66 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 21 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 91 tok/s Pro
Kimi K2 202 tok/s Pro
GPT OSS 120B 468 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Linking the mixing times of random walks on static and dynamic random graphs (2012.11012v2)

Published 20 Dec 2020 in math.PR

Abstract: This paper considers non-backtracking random walks on random graphs generated according to the configuration model. The quantity of interest is the scaling of the mixing time of the random walk as the number of vertices of the random graph tends to infinity. Subject to mild general conditions, we link two mixing times: one for a static version of the random graph, the other for a class of dynamic versions of the random graph in which the edges are randomly rewired but the degrees are preserved. The link is provided by the probability that the random walk has not yet stepped along a previously rewired edge. We use this link to compute the scaling of the mixing time for three specific classes of random rewirings. Depending on the speed and the range of the rewiring relative to the current location of the random walk, the mixing time may exhibit no cut-off, one-sided cut-off or two-sided cut-off, a trichotomy that was also found in earlier work. Interestingly, for a class of dynamics that are `mesoscopic', i.e., non-local and non-global, we find new behaviour with six subregimes. Proofs are built on a new and flexible coupling scheme, in combination with sharp estimates on the degrees encountered by the random walk in the static and the dynamic version of the random graph. Some of these estimates require sharp control on possible short-cuts in the graph between the edges that are traversed by the random walk.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.