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^*

^*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語	English
ホスト出版物のタイトル	Bio-Inspired Models of Network, Information, and Computing Systems - 5th International ICST Conference, BIONETICS 2010, Revised Selected Papers
ページ	497-504
ページ数	8
DOI	https://doi.org/10.1007/978-3-642-32615-8_47
出版ステータス	Published - 2012
外部発表	はい
イベント	5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010 - Boston, MA, United States 継続期間: 2010 12月 1 → 2010 12月 3

出版物シリーズ

名前	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
巻	87 LNICST
ISSN（印刷版）	1867-8211

Other

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

ASJC Scopus subject areas

コンピュータネットワークおよび通信

文献へのアクセス

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

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引用スタイル

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

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).

研究成果: Conference 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. 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. 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

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

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