An Effective Algorithm for Optimizing Surprise in Network Community Detection

Many methods have been proposed to detect communities/modules in various networks such as biological molecular networks and disease networks, while optimizing statistical measures for community structures is one of the most popular ways for community detection. Surprise, which is a statistical measu...

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Autores principales:	Yan-Ni Tang, Ju Xiang, Yuan-Yuan Gao, Zhi-Zhong Wang, Hui-Jia Li, Shi Chen, Yan Zhang, Jian-Ming Li, Yong-Hong Tang, Yong-Jun Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Complex networks community detection Surprise disease module disease gene Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b962e79c8ed34d83b0db87ef3b15e781
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spelling	oai:doaj.org-article:b962e79c8ed34d83b0db87ef3b15e7812021-12-02T00:00:10ZAn Effective Algorithm for Optimizing Surprise in Network Community Detection2169-353610.1109/ACCESS.2019.2946080https://doaj.org/article/b962e79c8ed34d83b0db87ef3b15e7812019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8861328/https://doaj.org/toc/2169-3536Many methods have been proposed to detect communities/modules in various networks such as biological molecular networks and disease networks, while optimizing statistical measures for community structures is one of the most popular ways for community detection. Surprise, which is a statistical measure of interest for community detection, has good performance in many networks, but it still encounters the resolution limit in some cases and it is hard to be optimized due to its strong nonlinearity. Here, we discussed the resolution limit of Surprise by a phase diagram in community-partition transition, and then proposed an improved algorithm for Surprise optimization by introducing three effective strategies: a pre-processing of topological structure based on local random walks (Pre_TS), a pre-processing of community partition (Pre_CS), and a post-processing of community partition (Post_CS). By a series of experimental tests in various networks, we show that Pre_TS can effectively enhance the resolution of Surprise, Pre_CS and Post_CS can improve the optimization performance in different aspects, and as expected, the combination of these strategies can more effectively enhance the ability of Surprise to detect communities in complex networks. Finally, we displayed the effectiveness of the improved algorithm for Surprise optimization in several real-world networks, and applied the algorithm to the analysis of disease-related networks in computational biology.Yan-Ni TangJu XiangYuan-Yuan GaoZhi-Zhong WangHui-Jia LiShi ChenYan ZhangJian-Ming LiYong-Hong TangYong-Jun ChenIEEEarticleComplex networkscommunity detectionSurprisedisease moduledisease geneElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 148814-148827 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Complex networks community detection Surprise disease module disease gene Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Complex networks community detection Surprise disease module disease gene Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yan-Ni Tang Ju Xiang Yuan-Yuan Gao Zhi-Zhong Wang Hui-Jia Li Shi Chen Yan Zhang Jian-Ming Li Yong-Hong Tang Yong-Jun Chen An Effective Algorithm for Optimizing Surprise in Network Community Detection
description	Many methods have been proposed to detect communities/modules in various networks such as biological molecular networks and disease networks, while optimizing statistical measures for community structures is one of the most popular ways for community detection. Surprise, which is a statistical measure of interest for community detection, has good performance in many networks, but it still encounters the resolution limit in some cases and it is hard to be optimized due to its strong nonlinearity. Here, we discussed the resolution limit of Surprise by a phase diagram in community-partition transition, and then proposed an improved algorithm for Surprise optimization by introducing three effective strategies: a pre-processing of topological structure based on local random walks (Pre_TS), a pre-processing of community partition (Pre_CS), and a post-processing of community partition (Post_CS). By a series of experimental tests in various networks, we show that Pre_TS can effectively enhance the resolution of Surprise, Pre_CS and Post_CS can improve the optimization performance in different aspects, and as expected, the combination of these strategies can more effectively enhance the ability of Surprise to detect communities in complex networks. Finally, we displayed the effectiveness of the improved algorithm for Surprise optimization in several real-world networks, and applied the algorithm to the analysis of disease-related networks in computational biology.
format	article
author	Yan-Ni Tang Ju Xiang Yuan-Yuan Gao Zhi-Zhong Wang Hui-Jia Li Shi Chen Yan Zhang Jian-Ming Li Yong-Hong Tang Yong-Jun Chen
author_facet	Yan-Ni Tang Ju Xiang Yuan-Yuan Gao Zhi-Zhong Wang Hui-Jia Li Shi Chen Yan Zhang Jian-Ming Li Yong-Hong Tang Yong-Jun Chen
author_sort	Yan-Ni Tang
title	An Effective Algorithm for Optimizing Surprise in Network Community Detection
title_short	An Effective Algorithm for Optimizing Surprise in Network Community Detection
title_full	An Effective Algorithm for Optimizing Surprise in Network Community Detection
title_fullStr	An Effective Algorithm for Optimizing Surprise in Network Community Detection
title_full_unstemmed	An Effective Algorithm for Optimizing Surprise in Network Community Detection
title_sort	effective algorithm for optimizing surprise in network community detection
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/b962e79c8ed34d83b0db87ef3b15e781
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An Effective Algorithm for Optimizing Surprise in Network Community Detection

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