Identifying Affinity Groups of Researchers in FEP through the Application of Community Detection ...

102 Pages·2017·2.51 MB·English

IDENTIFYING AFFINITY GROUPS OF RESEARCHERS IN
FEP THROUGH THE APPLICATION OF COMMUNITY
DETECTION ALGORITHMS
by
André Martinez Candeias Lima
Thesis for the Master’s Degree in Modelling, Data Analysis
and Decision Support Systems
Supervised by
Prof. João Gama
Prof. Pavel Brazdil
2017

Biographical note
André Martinez Candeias Lima was born in Porto in September 1992. After graduating
fromEscolaSecundáriaFilipadeVilhena,heenrolledin2010intheSchoolofEconomics
andManagementofUniversityofPorto,wherehewouldconcludehisBachelor’sDegree
in Economics in July 2013. Two years later he enrolled once again in the same Faculty,
this time in the Master’s Degree in Modelling, Data Analysis and Decision Support Sys-
tems. From September 2016 to March 2017 he also worked as a Functional Analyst at
OSI,acompanythatbelongstoGrupoAmorim.
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Acknowledgements
Iwouldliketothankeveryonewhocontributedtothisproject. Firstandforemost,mysu-
pervisors Professor João Gama and particularly Professor Pavel Brazdil for his unending
support,wisdomandenthusiasmtodrivethisprojectforward.
IwouldalsoliketothankRuiSarmentoandLuísTrigo,whoprovidedthetoolAffinity
Miner and who helped me along the way. I would like to thank the team at Authenticus
for providing the data and the members of the Faculty who showed their interest in this
project.
I would like to thank everyone at OSI for the support given in the months I worked
there.
I would like to thank my friends and family for their support during this journey.
A special thanks goes out to my friends from OT:C, who gave me many moments of
distractionandfun.
I am specially grateful to my parents, my sister Jessica and my friends Filipa, Maria
João, Daniel, Mike and Sara, who were always available to hear me out and always kept
meontherightpathtofinishthisthesis.
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Resumo
Redes são utilizadas para representar agentes e as suas interações. Como centro de in-
vestigação, a Faculdade de Economia da Universidade do Porto pode beneficiar ao ser
representada como uma rede. Com recurso ao Affinity Miner podemos criar uma rede
onde cada nó é um investigador, e estes estão ligados de acordo com o grau de simila-
ridade entre os títulos das suas publicações. Podemos descobrir comunidades nesta rede
quenosdãonovasperspetivasrelativasàsuaestrutura.
Um dos objetivos deste estudo é descobrir uma boa representação da rede original.
Estatemumnúmeroelevadodeligações,eparaasimplificarsãoremovidasligaçõescom
base num limiar abaixo do qual as ligações são considerads fracas. Isto afeta os resulta-
dos obtidos com a aplicação dos algoritmos de deteção de comunidades, logo o limiar e
oalgoritmodevemseranalisadosconjuntamente. Indicadorescomuns,comoamodulari-
dade,nãoserevelaramadequadosparaestatarefa. Crieiumamedida,modularidadecom
penalização de componentes, que me permite determinar um intervalo para o limiar no
qual se obtêm bons resultados para os algoritmos. Analiso depois os algoritmos neste
intervaloeconcluoqueométododeLouvainalcançaamelhorperformance.
Determinar se existe uma estrutura pertinente na rede é também importante. Analiso
umaredesimplificadaedescubroqueosinvestigadoresmaiscentraispertencemaoagru-
pamento científico de Economia. O método de Louvain obtém 9 comunidades, que estão
na sua maioria preenchidas por investigadores que pertencem ao mesmo grupo cientí-
fico. Na minha opinião e na de alguns membros da Faculdade, a estrutura descoberta é
pertinenteepermiteobternovasperspetivasemrelaçãoàorganizaçãodaFaculdade.
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Abstract
Networks are used to represent agents and their interactions. As an investigation centre,
the School of Economics and Management of the University of Porto can benefit from
being represented as a network. Using Affinity Miner we can create a network where
each node is a researcher, and they are connected by the level of similarity of the titles of
their publications. Communities can be discovered in this network, giving us insight into
itsstructure.
One of the objectives of this study is to discover a good representation of the original
network. It has a high number of links, and to simplify it links are removed based on a
threshold under which links are considered weak. This affects the results obtained when
applying community detection algorithms, so both the threshold and algorithms have to
be analysed together. Common indicators, such as modularity, prove to be inadequate
for this task. I have created a modularity with component penalty measure which allows
me to determine an interval of thresholds that provide good results from the algorithms.
I then test the algorithms for this particular interval and conclude that Louvain’s method
achievesthebestperformance.
Determining if a pertinent structure is present in the network is also important. I
analyse a simplified network and discover that the most central researchers are from the
scientific group of Economics. I then apply Louvain’s method and obtain 9 communities.
Eachofthesetendstobefilledwithresearchersfromthesamescientificgroups. Basedon
myopinionandthatofsomemembersoftheFaculty,thestructurediscoveredispertinent
andprovidesinterestinginsightsintotheFaculty’sorganisation.
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