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Journal of Statistical Physics

, Volume 172, Issue 4, pp 1127–1146 | Cite as

Lyapunov-Based Anomaly Detection in Highly-Clustered Networks

  • Diego RuizEmail author
  • Jorge Finke
Article
  • 66 Downloads

Abstract

Network formation models explain the dynamics of the structure of connections using mechanisms that operate under different principles for establishing and removing edges. The Jackson–Rogers model is a generic framework that applies the principle of triadic closure to networks that grow by the addition of new nodes and new edges over time. Past work describes the limit distribution of the in-degree of the nodes based on a continuous-time approximation. Here, we introduce a discrete-time approach of the dynamics of the in- and out-degree distributions of a variation of the model. Furthermore, we characterize the limit distributions and the expected value of the average degree as equilibria, and prove that the equilibria are asymptotically stable. Finally, we use the stability properties of the model to propose a detection criterion for anomalies in the edge formation process.

Keywords

Network formation models Stability Anomalous event detection 

Mathematics Subject Classification

05C82 37B25 94C12 

Notes

Acknowledgements

This research was supported in part by the Center of Excellence and Appropriation in Big Data and Data Analytics (CAOBf at the Pontificia Universidad Javeriana, the Ministry of Information Technologies and Telecommunications of Colombia (MinTIC), and the Colombian Administrative Department of Science, Technology and Innovation (COLCIENCIAS), under Grant No. FP44842-546-2015.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad del CaucaPopayánColombia
  2. 2.Universidad del ValleCaliColombia
  3. 3.Pontificia Universidad JaverianaCaliColombia

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