An algorithm for automatically discovering dynamical rules of adaptive network evolution from empirical data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

An algorithm is proposed for automatic discovery of a set of dynamical rules that best captures both state transition and topological transformation in the empirical data showing time evolution of adaptive networks. Graph rewriting systems are used as the basic model framework to represent state transition and topological transformation simultaneously. Network evolution is formulated in two phases: extraction and replacement of subnetworks. For each phase, multiple methods of rule discovery are proposed and will be explored. This paper reports the basic architecture of the algorithm, as well as its implementation and evaluation plan.

Original languageEnglish
Title of host publicationBio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers
Pages497-504
Number of pages8
Volume87 LNICST
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010 - Boston, MA, United States
Duration: 2010 Dec 12010 Dec 3

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
Volume87 LNICST
ISSN (Print)1867-8211

Other

Other5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010
CountryUnited States
CityBoston, MA
Period10/12/110/12/3

Keywords

  • Adaptive networks
  • algorithm
  • automatic rule discovery
  • generative network automata
  • graph rewriting systems

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Sayama, H. (2012). An algorithm for automatically discovering dynamical rules of adaptive network evolution from empirical data. In Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers (Vol. 87 LNICST, pp. 497-504). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering; Vol. 87 LNICST). https://doi.org/10.1007/978-3-642-32615-8_47

An algorithm for automatically discovering dynamical rules of adaptive network evolution from empirical data. / Sayama, Hiroki.

Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers. Vol. 87 LNICST 2012. p. 497-504 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering; Vol. 87 LNICST).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sayama, H 2012, An algorithm for automatically discovering dynamical rules of adaptive network evolution from empirical data. in Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers. vol. 87 LNICST, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, vol. 87 LNICST, pp. 497-504, 5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010, Boston, MA, United States, 10/12/1. https://doi.org/10.1007/978-3-642-32615-8_47
Sayama H. An algorithm for automatically discovering dynamical rules of adaptive network evolution from empirical data. In Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers. Vol. 87 LNICST. 2012. p. 497-504. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). https://doi.org/10.1007/978-3-642-32615-8_47
Sayama, Hiroki. / An algorithm for automatically discovering dynamical rules of adaptive network evolution from empirical data. Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers. Vol. 87 LNICST 2012. pp. 497-504 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
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