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

Hiroki Sayama

doi:10.1007/978-3-642-32615-8_47

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

Hiroki Sayama

Watson Dean's Office

Binghamton University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 language	English
Title of host publication	Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers
Pages	497-504
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-32615-8_47
State	Published - 2012
Event	5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010 - Boston, MA, United States Duration: Dec 1 2010 → Dec 3 2010

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
Volume	87 LNICST

Conference

Conference	5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010
Country/Territory	United States
City	Boston, MA
Period	12/1/10 → 12/3/10

Keywords

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

Access to Document

10.1007/978-3-642-32615-8_47

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 (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

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. 2012. pp. 497-504 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

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title = "An algorithm for automatically discovering dynamical rules of adaptive network evolution from empirical data",

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.",

keywords = "Adaptive networks, algorithm, automatic rule discovery, generative network automata, graph rewriting systems",

author = "Hiroki Sayama",

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series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering",

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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. 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, 12/1/10. 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. 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 proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

KW - Adaptive networks

KW - algorithm

KW - automatic rule discovery

KW - generative network automata

KW - graph rewriting systems

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DO - 10.1007/978-3-642-32615-8_47

M3 - Conference contribution

SN - 9783642326141

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering

SP - 497

EP - 504

BT - Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers

T2 - 5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010

Y2 - 1 December 2010 through 3 December 2010

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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. 2012. p. 497-504. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). doi: 10.1007/978-3-642-32615-8_47

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

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