Identification of important nodes in artificial bio-molecular networks

Pei Wang; Xinghuo Yu; Jinhu Lu; Aimin Chen

doi:10.1109/ISCAS.2014.6865373

Identification of important nodes in artificial bio-molecular networks

Pei Wang
, Xinghuo Yu
, Jinhu Lu
, Aimin Chen

Henan University
Royal Melbourne Institute of Technology University
Southeast University, Nanjing
Chinese Academy of Sciences

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

5 Scopus citations

Abstract

Identification of important nodes is an emerging hot topic in complex networks over the last few decades. The so-called important nodes are hub, influential nodes, leaders, and so on. To characterize the importance of nodes, various indexes are introduced in complex networks, such as degree, closeness, betweenness, k-shell, and principal component analysis based on the adjacency matrix. By using the above indexes and multivariate statistical analysis technique, this paper aims at developing a new approach to identify the important nodes in artificial bio-molecular networks generated from the duplication-divergence (DD) model. In particular, the statistical characteristics of important nodes are also investigated. The above results shed light on the potential real-world applications in bio-molecular networks, such as deducing the genes related to the specific disease.

Original language	English
Title of host publication	2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1267-1270
Number of pages	4
ISBN (Print)	9781479934324
DOIs	https://doi.org/10.1109/ISCAS.2014.6865373
State	Published - 2014
Externally published	Yes
Event	2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 - Melbourne, VIC, Australia Duration: 1 Jun 2014 → 5 Jun 2014

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Conference

Conference	2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
Country/Territory	Australia
City	Melbourne, VIC
Period	1/06/14 → 5/06/14

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10.1109/ISCAS.2014.6865373

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Wang, P., Yu, X., Lu, J., & Chen, A. (2014). Identification of important nodes in artificial bio-molecular networks. In 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 (pp. 1267-1270). Article 6865373 (Proceedings - IEEE International Symposium on Circuits and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2014.6865373

@inproceedings{e704df4330f04acd96763bf1ebbd45c3,

title = "Identification of important nodes in artificial bio-molecular networks",

abstract = "Identification of important nodes is an emerging hot topic in complex networks over the last few decades. The so-called important nodes are hub, influential nodes, leaders, and so on. To characterize the importance of nodes, various indexes are introduced in complex networks, such as degree, closeness, betweenness, k-shell, and principal component analysis based on the adjacency matrix. By using the above indexes and multivariate statistical analysis technique, this paper aims at developing a new approach to identify the important nodes in artificial bio-molecular networks generated from the duplication-divergence (DD) model. In particular, the statistical characteristics of important nodes are also investigated. The above results shed light on the potential real-world applications in bio-molecular networks, such as deducing the genes related to the specific disease.",

author = "Pei Wang and Xinghuo Yu and Jinhu Lu and Aimin Chen",

year = "2014",

doi = "10.1109/ISCAS.2014.6865373",

language = "英语",

isbn = "9781479934324",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1267--1270",

booktitle = "2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014",

address = "美国",

note = "2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 ; Conference date: 01-06-2014 Through 05-06-2014",

}

Wang, P, Yu, X, Lu, J & Chen, A 2014, Identification of important nodes in artificial bio-molecular networks. in 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014., 6865373, Proceedings - IEEE International Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 1267-1270, 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014, Melbourne, VIC, Australia, 1/06/14. https://doi.org/10.1109/ISCAS.2014.6865373

Identification of important nodes in artificial bio-molecular networks. / Wang, Pei; Yu, Xinghuo; Lu, Jinhu et al.
2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1267-1270 6865373 (Proceedings - IEEE International Symposium on Circuits and Systems).

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

TY - GEN

T1 - Identification of important nodes in artificial bio-molecular networks

AU - Wang, Pei

AU - Yu, Xinghuo

AU - Lu, Jinhu

AU - Chen, Aimin

PY - 2014

Y1 - 2014

N2 - Identification of important nodes is an emerging hot topic in complex networks over the last few decades. The so-called important nodes are hub, influential nodes, leaders, and so on. To characterize the importance of nodes, various indexes are introduced in complex networks, such as degree, closeness, betweenness, k-shell, and principal component analysis based on the adjacency matrix. By using the above indexes and multivariate statistical analysis technique, this paper aims at developing a new approach to identify the important nodes in artificial bio-molecular networks generated from the duplication-divergence (DD) model. In particular, the statistical characteristics of important nodes are also investigated. The above results shed light on the potential real-world applications in bio-molecular networks, such as deducing the genes related to the specific disease.

AB - Identification of important nodes is an emerging hot topic in complex networks over the last few decades. The so-called important nodes are hub, influential nodes, leaders, and so on. To characterize the importance of nodes, various indexes are introduced in complex networks, such as degree, closeness, betweenness, k-shell, and principal component analysis based on the adjacency matrix. By using the above indexes and multivariate statistical analysis technique, this paper aims at developing a new approach to identify the important nodes in artificial bio-molecular networks generated from the duplication-divergence (DD) model. In particular, the statistical characteristics of important nodes are also investigated. The above results shed light on the potential real-world applications in bio-molecular networks, such as deducing the genes related to the specific disease.

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DO - 10.1109/ISCAS.2014.6865373

M3 - 会议稿件

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SN - 9781479934324

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

SP - 1267

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BT - 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014

Y2 - 1 June 2014 through 5 June 2014

ER -

Identification of important nodes in artificial bio-molecular networks

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