Collaboration signatures reveal scientific impact

Yuxiao Dong, Reid A. Johnson, Yang Yang, Nitesh V. Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Collaboration is an integral element of the scientific process that often leads to findings with significant impact. While extensive efforts have been devoted to quantifying and predicting research impact, the question of how collaborative behavior influences scientific impact remains unaddressed. In this work, we study the interplay between scientists' collaboration signatures and their scientific impact. As the basis of our study, we employ an ArnetMiner dataset with more than 1.7 million authors and 2 million papers spanning over 60 years. We formally define a scientist's collaboration signature as the distribution of collaboration strengths with each collaborator in his or her academic ego network, which is quantified by four measures: sociability, dependence, diversity, and self-collaboration. We then demonstrate that the collaboration signature allows us to effectively distinguish between researchers with dissimilar levels of scientific impact. We also discover that, even from the early stages of one's researcher career, a scientist's collaboration signature can help to reveal his or her future scientific impact. Finally, we find that as a representative group of outstanding computer scientists, Turing Award winners collectively produce distinctive collaboration signatures throughout the entirety of their careers. Our conclusions on the relationship between collaboration signatures and scientific impact give rise to important implications for researchers who wish to expand their scientific impact and more effectively stand on the shoulders of "collaborators".

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
EditorsJian Pei, Jie Tang, Fabrizio Silvestri
PublisherAssociation for Computing Machinery, Inc
Pages480-487
Number of pages8
ISBN (Electronic)9781450338547
DOIs
StatePublished - Aug 25 2015
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
Duration: Aug 25 2015Aug 28 2015

Publication series

NameProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Other

OtherIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
CountryFrance
CityParis
Period8/25/158/28/15

Keywords

  • Academic social network
  • Collaboration signature
  • Science of science
  • Scientific impact
  • Scientific success

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

Cite this

Dong, Y., Johnson, R. A., Yang, Y., & Chawla, N. V. (2015). Collaboration signatures reveal scientific impact. In J. Pei, J. Tang, & F. Silvestri (Eds.), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 480-487). (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2808846
Dong, Yuxiao ; Johnson, Reid A. ; Yang, Yang ; Chawla, Nitesh V. / Collaboration signatures reveal scientific impact. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. editor / Jian Pei ; Jie Tang ; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. pp. 480-487 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).
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abstract = "Collaboration is an integral element of the scientific process that often leads to findings with significant impact. While extensive efforts have been devoted to quantifying and predicting research impact, the question of how collaborative behavior influences scientific impact remains unaddressed. In this work, we study the interplay between scientists' collaboration signatures and their scientific impact. As the basis of our study, we employ an ArnetMiner dataset with more than 1.7 million authors and 2 million papers spanning over 60 years. We formally define a scientist's collaboration signature as the distribution of collaboration strengths with each collaborator in his or her academic ego network, which is quantified by four measures: sociability, dependence, diversity, and self-collaboration. We then demonstrate that the collaboration signature allows us to effectively distinguish between researchers with dissimilar levels of scientific impact. We also discover that, even from the early stages of one's researcher career, a scientist's collaboration signature can help to reveal his or her future scientific impact. Finally, we find that as a representative group of outstanding computer scientists, Turing Award winners collectively produce distinctive collaboration signatures throughout the entirety of their careers. Our conclusions on the relationship between collaboration signatures and scientific impact give rise to important implications for researchers who wish to expand their scientific impact and more effectively stand on the shoulders of {"}collaborators{"}.",
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Dong, Y, Johnson, RA, Yang, Y & Chawla, NV 2015, Collaboration signatures reveal scientific impact. in J Pei, J Tang & F Silvestri (eds), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Association for Computing Machinery, Inc, pp. 480-487, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, 8/25/15. https://doi.org/10.1145/2808797.2808846

Collaboration signatures reveal scientific impact. / Dong, Yuxiao; Johnson, Reid A.; Yang, Yang; Chawla, Nitesh V.

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. ed. / Jian Pei; Jie Tang; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. p. 480-487 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Dong, Yuxiao

AU - Johnson, Reid A.

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AU - Chawla, Nitesh V.

PY - 2015/8/25

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N2 - Collaboration is an integral element of the scientific process that often leads to findings with significant impact. While extensive efforts have been devoted to quantifying and predicting research impact, the question of how collaborative behavior influences scientific impact remains unaddressed. In this work, we study the interplay between scientists' collaboration signatures and their scientific impact. As the basis of our study, we employ an ArnetMiner dataset with more than 1.7 million authors and 2 million papers spanning over 60 years. We formally define a scientist's collaboration signature as the distribution of collaboration strengths with each collaborator in his or her academic ego network, which is quantified by four measures: sociability, dependence, diversity, and self-collaboration. We then demonstrate that the collaboration signature allows us to effectively distinguish between researchers with dissimilar levels of scientific impact. We also discover that, even from the early stages of one's researcher career, a scientist's collaboration signature can help to reveal his or her future scientific impact. Finally, we find that as a representative group of outstanding computer scientists, Turing Award winners collectively produce distinctive collaboration signatures throughout the entirety of their careers. Our conclusions on the relationship between collaboration signatures and scientific impact give rise to important implications for researchers who wish to expand their scientific impact and more effectively stand on the shoulders of "collaborators".

AB - Collaboration is an integral element of the scientific process that often leads to findings with significant impact. While extensive efforts have been devoted to quantifying and predicting research impact, the question of how collaborative behavior influences scientific impact remains unaddressed. In this work, we study the interplay between scientists' collaboration signatures and their scientific impact. As the basis of our study, we employ an ArnetMiner dataset with more than 1.7 million authors and 2 million papers spanning over 60 years. We formally define a scientist's collaboration signature as the distribution of collaboration strengths with each collaborator in his or her academic ego network, which is quantified by four measures: sociability, dependence, diversity, and self-collaboration. We then demonstrate that the collaboration signature allows us to effectively distinguish between researchers with dissimilar levels of scientific impact. We also discover that, even from the early stages of one's researcher career, a scientist's collaboration signature can help to reveal his or her future scientific impact. Finally, we find that as a representative group of outstanding computer scientists, Turing Award winners collectively produce distinctive collaboration signatures throughout the entirety of their careers. Our conclusions on the relationship between collaboration signatures and scientific impact give rise to important implications for researchers who wish to expand their scientific impact and more effectively stand on the shoulders of "collaborators".

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Dong Y, Johnson RA, Yang Y, Chawla NV. Collaboration signatures reveal scientific impact. In Pei J, Tang J, Silvestri F, editors, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc. 2015. p. 480-487. (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). https://doi.org/10.1145/2808797.2808846