Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network

Alexander Semenov; Alexander Veremyev; Alexander Nikolaev; Eduardo L. Pasiliao; Vladimir Boginski

doi:10.1007/978-3-030-04648-4_37

Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network

Alexander Semenov
, Alexander Veremyev
, Alexander Nikolaev
, Eduardo L. Pasiliao
, Vladimir Boginski

Department of Industrial and Systems Engineering

University of Jyväskylä
University of Central Florida
Air Force Research Laboratory

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

1 Scopus citations

Abstract

Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual, but also subsequent academic advising records of those students. We also define and calculate the extensions of the proposed indices that account for student co-advising (referred to as “adjusted a-indices”). Finally, we extend the proposed metrics to ranking universities and countries with respect to their “collective” advising impacts. To illustrate the proposed metrics, we consider the social network of over 200,000 mathematicians (as of July 2018) constructed using the Mathematics Genealogy Project data: the network nodes represent the mathematicians who have completed Ph.D. degrees, and the directed edges connect advisors with their students.

Original language	English
Title of host publication	Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings
Editors	My T. Thai, Xuemin Chen, Wei Wayne Li, Arunabha Sen
Publisher	Springer Verlag
Pages	437-449
Number of pages	13
ISBN (Print)	9783030046477
DOIs	https://doi.org/10.1007/978-3-030-04648-4_37
State	Published - 2018
Event	7th International Conference on Computational Data and Social Networks, CSoNet 2018 - Shanghai, China Duration: Dec 18 2018 → Dec 20 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11280 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Computational Data and Social Networks, CSoNet 2018
Country/Territory	China
City	Shanghai
Period	12/18/18 → 12/20/18

Keywords

a-indices
Big data
Mathematics genealogy project
Scientific advising impact
Social networks

Access to Document

10.1007/978-3-030-04648-4_37

Cite this

Semenov, A., Veremyev, A., Nikolaev, A., Pasiliao, E. L., & Boginski, V. (2018). Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network. In M. T. Thai, X. Chen, W. W. Li, & A. Sen (Eds.), Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings (pp. 437-449). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11280 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04648-4_37

Semenov, Alexander ; Veremyev, Alexander ; Nikolaev, Alexander et al. / Ranking academic advisors : analyzing scientific advising impact using mathgenealogy social network. Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. editor / My T. Thai ; Xuemin Chen ; Wei Wayne Li ; Arunabha Sen. Springer Verlag, 2018. pp. 437-449 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7c4e085888d94562b8b795bfc582a86f,

title = "Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network",

abstract = "Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual, but also subsequent academic advising records of those students. We also define and calculate the extensions of the proposed indices that account for student co-advising (referred to as “adjusted a-indices”). Finally, we extend the proposed metrics to ranking universities and countries with respect to their “collective” advising impacts. To illustrate the proposed metrics, we consider the social network of over 200,000 mathematicians (as of July 2018) constructed using the Mathematics Genealogy Project data: the network nodes represent the mathematicians who have completed Ph.D. degrees, and the directed edges connect advisors with their students.",

keywords = "a-indices, Big data, Mathematics genealogy project, Scientific advising impact, Social networks",

author = "Alexander Semenov and Alexander Veremyev and Alexander Nikolaev and Pasiliao, \{Eduardo L.\} and Vladimir Boginski",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 7th International Conference on Computational Data and Social Networks, CSoNet 2018 ; Conference date: 18-12-2018 Through 20-12-2018",

year = "2018",

doi = "10.1007/978-3-030-04648-4\_37",

language = "English",

isbn = "9783030046477",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "437--449",

editor = "Thai, \{My T.\} and Xuemin Chen and Li, \{Wei Wayne\} and Arunabha Sen",

booktitle = "Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings",

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Semenov, A, Veremyev, A, Nikolaev, A, Pasiliao, EL & Boginski, V 2018, Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network. in MT Thai, X Chen, WW Li & A Sen (eds), Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11280 LNCS, Springer Verlag, pp. 437-449, 7th International Conference on Computational Data and Social Networks, CSoNet 2018, Shanghai, China, 12/18/18. https://doi.org/10.1007/978-3-030-04648-4_37

Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network. / Semenov, Alexander; Veremyev, Alexander; Nikolaev, Alexander et al.
Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. ed. / My T. Thai; Xuemin Chen; Wei Wayne Li; Arunabha Sen. Springer Verlag, 2018. p. 437-449 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11280 LNCS).

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

TY - GEN

T1 - Ranking academic advisors

T2 - 7th International Conference on Computational Data and Social Networks, CSoNet 2018

AU - Semenov, Alexander

AU - Veremyev, Alexander

AU - Nikolaev, Alexander

AU - Pasiliao, Eduardo L.

AU - Boginski, Vladimir

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual, but also subsequent academic advising records of those students. We also define and calculate the extensions of the proposed indices that account for student co-advising (referred to as “adjusted a-indices”). Finally, we extend the proposed metrics to ranking universities and countries with respect to their “collective” advising impacts. To illustrate the proposed metrics, we consider the social network of over 200,000 mathematicians (as of July 2018) constructed using the Mathematics Genealogy Project data: the network nodes represent the mathematicians who have completed Ph.D. degrees, and the directed edges connect advisors with their students.

AB - Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual, but also subsequent academic advising records of those students. We also define and calculate the extensions of the proposed indices that account for student co-advising (referred to as “adjusted a-indices”). Finally, we extend the proposed metrics to ranking universities and countries with respect to their “collective” advising impacts. To illustrate the proposed metrics, we consider the social network of over 200,000 mathematicians (as of July 2018) constructed using the Mathematics Genealogy Project data: the network nodes represent the mathematicians who have completed Ph.D. degrees, and the directed edges connect advisors with their students.

KW - a-indices

KW - Big data

KW - Mathematics genealogy project

KW - Scientific advising impact

KW - Social networks

UR - https://www.scopus.com/pages/publications/85059069570

U2 - 10.1007/978-3-030-04648-4_37

DO - 10.1007/978-3-030-04648-4_37

M3 - Conference contribution

AN - SCOPUS:85059069570

SN - 9783030046477

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 437

EP - 449

BT - Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings

A2 - Thai, My T.

A2 - Chen, Xuemin

A2 - Li, Wei Wayne

A2 - Sen, Arunabha

PB - Springer Verlag

Y2 - 18 December 2018 through 20 December 2018

ER -

Semenov A, Veremyev A, Nikolaev A, Pasiliao EL, Boginski V. Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network. In Thai MT, Chen X, Li WW, Sen A, editors, Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. Springer Verlag. 2018. p. 437-449. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04648-4_37

Ranking academic advisors: analyzing scientific advising impact using mathgenealogy social network

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Keywords

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