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MiS Preprint Repository

We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint
30/2016

Forman-Ricci flow for change detection in large dynamic data sets

Melanie Weber, Jürgen Jost and Emil Saucan

Abstract

We present a viable geometric solution for the detection of dynamic effects in complex networks. Building on Forman’s discretization of the classical notion of Ricci curvature, we introduce a novel geometric method to characterize different types of real-world networks with an emphasis on peer-to-peer networks. We study the classical Ricci-flow in a network-theoretic setting and introduce an analytic tool for characterizing dynamic effects. The formalism suggests a novel computational method for change detection and the identification of fast evolving network regions and yields insights into topological properties and the structure of the underlying data.

Received:
Mar 28, 2016
Published:
May 2, 2016
MSC Codes:
68, 94, 52
Keywords:
change detection, dynamic networks, Ricci flow, Forman curvature, complex systems

Related publications

inJournal
2016 Journal Open Access
Melanie Weber, Jürgen Jost and Emil Saucan

Forman-Ricci flow for change detection in large dynamic data sets

In: Axioms, 5 (2016) 4, p. 26