Investigating collaboration evolution in ubicomp research

Chao Ma; Zhiwen Yu; Fei Yi; Zhu Wang; Qingyang Li; Bin Guo

doi:10.1145/2968219.2971432

Investigating collaboration evolution in ubicomp research

Chao Ma, Zhiwen Yu, Fei Yi, Zhu Wang, Qingyang Li, Bin Guo

School of Computer Science

Northwestern Polytechnical University Xian

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

Abstract

During the past 17 years, UbiComp has grown into one of the most influential conferences. To further figure out how the contributors of UbiComp collaborate with each other, we first construct a collaboration network based on 765 UbiComp papers published since 1999. Afterwards, to examine the patterns of collaborations, a PCA-based time series segmentation scheme is proposed, which suggests 2009 as a break point. By applying comparative analysis, we then investigate the collaboration evolution in different periods and propose a new method to quantify the actual effect of each paper. Experimental results reveal the existence of four types of collaboration patterns and proved that, as UbiComp getting more collaborative, long-Term collaboration is more inclined to facilitate high quality achievements.

Original language	English
Title of host publication	UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Publisher	Association for Computing Machinery, Inc
Pages	161-164
Number of pages	4
ISBN (Electronic)	9781450344623
DOIs	https://doi.org/10.1145/2968219.2971432
State	Published - 12 Sep 2016
Event	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany Duration: 12 Sep 2016 → 16 Sep 2016

Publication series

Name	UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Conference

Conference	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
Country/Territory	Germany
City	Heidelberg
Period	12/09/16 → 16/09/16

Keywords

Collaboration Evolution
Collaboration Pattern
Network Analysis
UbiComp

Access to Document

10.1145/2968219.2971432

Cite this

Ma, C., Yu, Z., Yi, F., Wang, Z., Li, Q., & Guo, B. (2016). Investigating collaboration evolution in ubicomp research. In UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 161-164). (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2968219.2971432

Ma, Chao ; Yu, Zhiwen ; Yi, Fei et al. / Investigating collaboration evolution in ubicomp research. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. pp. 161-164 (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

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title = "Investigating collaboration evolution in ubicomp research",

abstract = "During the past 17 years, UbiComp has grown into one of the most influential conferences. To further figure out how the contributors of UbiComp collaborate with each other, we first construct a collaboration network based on 765 UbiComp papers published since 1999. Afterwards, to examine the patterns of collaborations, a PCA-based time series segmentation scheme is proposed, which suggests 2009 as a break point. By applying comparative analysis, we then investigate the collaboration evolution in different periods and propose a new method to quantify the actual effect of each paper. Experimental results reveal the existence of four types of collaboration patterns and proved that, as UbiComp getting more collaborative, long-Term collaboration is more inclined to facilitate high quality achievements.",

keywords = "Collaboration Evolution, Collaboration Pattern, Network Analysis, UbiComp",

author = "Chao Ma and Zhiwen Yu and Fei Yi and Zhu Wang and Qingyang Li and Bin Guo",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 ; Conference date: 12-09-2016 Through 16-09-2016",

year = "2016",

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day = "12",

doi = "10.1145/2968219.2971432",

language = "英语",

series = "UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",

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Ma, C, Yu, Z, Yi, F, Wang, Z, Li, Q & Guo, B 2016, Investigating collaboration evolution in ubicomp research. in UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Inc, pp. 161-164, 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, 12/09/16. https://doi.org/10.1145/2968219.2971432

Investigating collaboration evolution in ubicomp research. / Ma, Chao; Yu, Zhiwen; Yi, Fei et al.
UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. p. 161-164 (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

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

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AB - During the past 17 years, UbiComp has grown into one of the most influential conferences. To further figure out how the contributors of UbiComp collaborate with each other, we first construct a collaboration network based on 765 UbiComp papers published since 1999. Afterwards, to examine the patterns of collaborations, a PCA-based time series segmentation scheme is proposed, which suggests 2009 as a break point. By applying comparative analysis, we then investigate the collaboration evolution in different periods and propose a new method to quantify the actual effect of each paper. Experimental results reveal the existence of four types of collaboration patterns and proved that, as UbiComp getting more collaborative, long-Term collaboration is more inclined to facilitate high quality achievements.

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Ma C, Yu Z, Yi F, Wang Z, Li Q, Guo B. Investigating collaboration evolution in ubicomp research. In UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc. 2016. p. 161-164. (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). doi: 10.1145/2968219.2971432

Investigating collaboration evolution in ubicomp research

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