a geographical analysis of knowledge production in computer science guilherme vale menezes nivio...
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A Geographical Analysis of Knowledge Production in Computer Science
Guilherme Vale Menezes
Nivio Ziviani
Alberto H. F. Laender
Virgílio Almeida
Federal University of Minas Gerais - Brazil
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LAboratory for Treating INformation (LATIN) – UFMG - Brazil
Summary
Introduction Data Gathering Results Conclusions
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The Problem
Study the characteristics of researchers of Computer Science graduate programs
30 graduate programs in 3 geographic regions Build collaboration social networks based on
DBLP We use several metrics of collaboration social
networks Giant Component Clustering Coefficient
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Steps
Comparison between 30 programs in 3 regions Comparison between 30 Computer Science fields Study of the interrelationship between fields Temporal analysis of the 3 regions and the fields
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Collaboration Network
Author
Collaboration
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Collaborations in DCC-UFMG
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Collaborations in DCC-UFMG
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Summary
Introduction Data Gathering Results Conclusions
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
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Data Gathering
Part of our data came from Perfil-CC project Objective of Perfil-CC: study Brazilian Computer
Science graduate programs A set of 30 programs was chosen Focus: comparison with North American programs Results supported public policies Data gathered in June 2007
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Brazilian Programs
8 graduate programs391 authors
PUC-Rio, UFRJ, UFPE, UFMG,USP-SP, USP-SC, UNICAMP, UFRGS
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Canadian and US Programs
16 graduate programs1,262 authors
British Columbia, Toronto, Waterloo, Brown, CalTech,CMU, Cornell, Harvard, Illinois, MIT, Princeton, Stanford, UC Berkeley, UTexas Austin, Washington, Wisconsin
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French, Swiss and UK Programs
6 graduate programs611 authors
ETH Zurich, Cambridge U., Imperial College, Oxford U., École Polytechnique, Paris VI
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Data Gathering Professors obtained from the departments’ websites
Publications from DBLP
Programs: 30Professors: 2,007Authors: 76,537Papers: 352,766Venues: 2,176
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Venue Classification
2,176 were classified (by humans) into 30 fields The list of fields was obtained from a poll The brazilian Computer Science research
community was consulted 312 researchers identified 30 different fields
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Computer Science Fields
Algorithms and Theory Applied Computing Artificial Intelligence Bioinformatics
Circuit Conception Comp. Architecture Comp. Graphics Comp. Networks and Distributed Systems
Computer Vision Data Mining Databases Embedded and Real-Time Systems
Formalisms, Logics and Semantics
Games and Entertainment
Geoinformatics Human-Comp. Interaction
Informatics in Education
Information Retrieval Information Systems Machine Learning
Modelling and Simulation
Natural Language Processing
Operation Systems Operational Research and Optimization
Programming Languages
Robotics, Automation and Control
Security and Privacy Software Engineering
Ubiquitous Comp. Web, Hypermedia, Multimedia
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Computer Science Fields
Algorithms and Theory Applied Computing Artificial Intelligence Bioinformatics
Circuit Conception Comp. Architecture Comp. Graphics Comp. Networks and Distributed Systems
Computer Vision Data Mining Databases Embedded and Real-Time Systems
Formalisms, Logics and Semantics
Games and Entertainment
Geoinformatics Human-Comp. Interaction
Informatics in Education
Information Retrieval Information Systems Machine Learning
Modelling and Simulation
Natural Language Processing
Operation Systems Operational Research and Optimization
Programming Languages
Robotics, Automation and Control
Security and Privacy Software Engineering
Ubiquitous Comp. Web, Hypermedia, Multimedia
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
Algorithms and Theory
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Computer Science Fields
Algorithms and Theory Applied Computing Artificial Intelligence Bioinformatics
Circuit Conception Comp. Architecture Comp. Graphics Comp. Networks and Distributed Systems
Computer Vision Data Mining Databases Embedded and Real-Time Systems
Formalisms, Logics and Semantics
Games and Entertainment
Geoinformatics Human-Comp. Interaction
Informatics in Education
Information Retrieval Information Systems Machine Learning
Modelling and Simulation
Natural Language Processing
Operation Systems Operational Research and Optimization
Programming Languages
Robotics, Automation and Control
Security and Privacy Software Engineering
Ubiquitous Comp. Web, Hypermedia, Multimedia
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
InformationRetrieval
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Computer Science Fields
Algorithms and Theory Applied Computing Artificial Intelligence Bioinformatics
Circuit Conception Comp. Architecture Comp. Graphics Comp. Networks and Distributed Systems
Computer Vision Data Mining Databases Embedded and Real-Time Systems
Formalisms, Logics and Semantics
Games and Entertainment
Geoinformatics Human-Comp. Interaction
Informatics in Education
Information Retrieval Information Systems Machine Learning
Modelling and Simulation
Natural Language Processing
Operation Systems Operational Research and Optimization
Programming Languages
Robotics, Automation and Control
Security and Privacy Software Engineering
Ubiquitous Comp. Web, Hypermedia, Multimedia
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
Bioinformatics
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Summary
Introduction Data Gathering Results Conclusions
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
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General Statistics
Br Ca-US Fr-Sw-UK
Authors 357 1,008 488
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General Statistics
Br Ca-US Fr-Sw-UK
Authors 357 1,008 488
Papers 4,405 40,039 8,764
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General Statistics
Br Ca-US Fr-Sw-UK
Authors 357 1,008 488
Papers 4,405 40,039 8,764
Paper/Author 16.06 45.89 19.85
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General Statistics
Br Ca-US Fr-Sw-UK
Authors 357 1,008 488
Papers 4,405 40,039 8,764
Paper/Author 16.06 45.89 19.85
Author/Paper 3.21 2.87 2.77
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Giant Component A connected component is a maximum connected
subgraph
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Giant Component
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
A connected component is a maximum connected subgraph
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Giant Component
Giant Component size = 5 / 11 = 0.45 = 45%
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
A connected component is a maximum connected subgraph
The largest connected component is the giant component
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Giant Component
Br Ca-US Fr-Sw-UK
0.7815 0.7827 0.2617
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Giant Component inside ProgramsBr Ca-US Fr-Sw-UK
PUC-Rio 0,83 Wisconsin 0,71 Oxford 0,46UFMG 0,76 Harvard 0,70 Imperial College 0,40
USP-SC 0,73 Cornell 0,66École
Polytechnique0,30
UFRGS 0,70 Stanford 0,66 ETH Zurich 0,26UFPE 0,68 Washington 0,65 Cambridge 0,25UFRJ 0,64 Illinois 0,63 Paris IV 0,12
UNICAMP 0,62 Princeton 0,63 - -USP-SP 0,43 B. Columbia 0,62 - -
- - U Texas 0,62 - -- - CMU 0,59 - -- - UC Berkeley 0,59 - -- - Brown 0,53 - -- - MIT 0,49 - -- - Waterloo 0,42 - -- - Caltech 0,21 - -
Average 0,67 Average 0,56 Average 0,30
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Clustering Coefficient
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Clustering Coefficient
Clustering coefficient of the network is the average clustering coefficient of its vertexes
The clustering coefficient is a measure of transitivity
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Clustering Coefficient
Br Ca-US Fr-Sw-UK
0.30 0.20 0.38
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Computer Science Fields
Clustering Coefficient below the average (87%) for fields closely related to Mathematics Algorithms and Theory (79%) Operational Reaseach and Optimization (83%) Formalisms, Logics and Semantics (83%)
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Interrelationship between Fields
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Interrelationship between Fields
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Giant Component Evolution
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Giant Component Evolution
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Giant Component Evolution
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Increase in the number of graduate programs in 1990s
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Giant Component Evolution
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Increase in government funding
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Giant Component Evolution
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A shift in policy: more support to research groups instead of individuals
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Giant Component Evolution
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Giant Component Evolution
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Giant Component Evolution
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Edges vs Vertices
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Br Ca-US Fr-Sw-UK
Inclination 1.39 1.84 1.23
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2 well-established fields Computer Architecture Databases
2 emerging fields Bioinformatics Geoinformatics
Clustering Coefficient Evolution
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Clustering Coefficient Evolution
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Clustering Coefficient Evolution
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Densification
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Summary
Introduction Data Gathering Results Conclusions
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
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Conclusions Analysis of the characteristics of researchers of
Computer Science graduate programs
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
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Analysis of the characteristics of researchers of Computer Science graduate programs
Differences in the collaboration network of Br, Ca-US and Fr-Sw-UK Giant component Clustering coefficient
Conclusions
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
![Page 50: A Geographical Analysis of Knowledge Production in Computer Science Guilherme Vale Menezes Nivio Ziviani Alberto H. F. Laender Virgílio Almeida gmenezes@dcc.ufmg.br](https://reader036.vdocuments.mx/reader036/viewer/2022070305/5514c5c855034640138b5af1/html5/thumbnails/50.jpg)
Analysis of the characteristics of researchers of Computer Science graduate programs
Differences in the collaboration network of Br, Ca-US and Fr-Sw-UK Giant component Clustering coefficient
Smaller clustering coefficient for areas more closely related to Mathematics
Conclusions
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
![Page 51: A Geographical Analysis of Knowledge Production in Computer Science Guilherme Vale Menezes Nivio Ziviani Alberto H. F. Laender Virgílio Almeida gmenezes@dcc.ufmg.br](https://reader036.vdocuments.mx/reader036/viewer/2022070305/5514c5c855034640138b5af1/html5/thumbnails/51.jpg)
Conclusions
Fast growth of the giant component in Brazil
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
![Page 52: A Geographical Analysis of Knowledge Production in Computer Science Guilherme Vale Menezes Nivio Ziviani Alberto H. F. Laender Virgílio Almeida gmenezes@dcc.ufmg.br](https://reader036.vdocuments.mx/reader036/viewer/2022070305/5514c5c855034640138b5af1/html5/thumbnails/52.jpg)
Conclusions
Fast growth of the giant component in Brazil
The number of edges grows faster than the number of vertices in the three regions; faster growth in Ca-US
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
![Page 53: A Geographical Analysis of Knowledge Production in Computer Science Guilherme Vale Menezes Nivio Ziviani Alberto H. F. Laender Virgílio Almeida gmenezes@dcc.ufmg.br](https://reader036.vdocuments.mx/reader036/viewer/2022070305/5514c5c855034640138b5af1/html5/thumbnails/53.jpg)
Conclusions
Fast growth of the giant component in Brazil
The number of edges grows faster than the number of vertices in the three regions; faster growth in Ca-US
Densification of emerging fields
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
![Page 54: A Geographical Analysis of Knowledge Production in Computer Science Guilherme Vale Menezes Nivio Ziviani Alberto H. F. Laender Virgílio Almeida gmenezes@dcc.ufmg.br](https://reader036.vdocuments.mx/reader036/viewer/2022070305/5514c5c855034640138b5af1/html5/thumbnails/54.jpg)
ReferencesLaender, Lucena, Maldonado, Souza e Silva, Ziviani. Assessing the Research and Education Quality of the Top Brazilian Graduate Programs. ACM SIGCSE Bulletin, 40:135-145, June 2008.
Martins, Gonçalves, Laender, Ziviani. Assessing the Quality of Scientific Conferences Based on Bibliographic Citations. Scientometrics, to appear. 2009.
LAboratory for Treating INformation (LATIN) – UFMG - Brazil
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Guilherme Vale Menezes
Nivio Ziviani
Alberto H. F. Laender
Virgílio Almeida
Federal University of Minas Gerais - Brazil
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