Forman-Ricci Flow for Change Detection in Large Dynamic Data Sets
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-pe...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2016-11-01
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Series: | Axioms |
Subjects: | |
Online Access: | http://www.mdpi.com/2075-1680/5/4/26 |
Summary: | 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 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. |
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ISSN: | 2075-1680 |