Design and Use of Anatomical Atlases for Radiotherapy

177 Pages·2017·12.71 MB·English

UNIVERSITYCOTED’AZUR
DOCTORAL SCHOOL STIC
SCIENCESETTECHNOLOGIESDEL’INFORMATION
ETDELACOMMUNICATION
P H D T H E S I S
toobtainthetitleof
PhD of Science
of the Université côte d’azur
Specialty : COMPUTER SCIENCE
Defendedby
Zide MENG
Temporal and semantic analysis of
richly typed social networks from
user-generated content sites on the
web
Thesis Advisor: Fabien GANDON and Catherine FARON-ZUCKER
prepared at INRIA Sophia Antipolis, WIMMICS Team
defendedonNov07,2016
Jury:
Reviewers: Pr. Frédérique LAFOREST - TélécomSaint-Etienne
Dr. John BRESLIN - NationalUniversityofIreland,Galway
Examiner: Pr. Martin ARNAUD - UniversitédeRennes1
Advisor: Dr. Fabien GANDON - INRIASophiaAntipolis
Co-Advisor: Dr. Catherine FARON-ZUCKER - UniversityNiceSophiaAntipolis
President: Pr. Frederic PRECIOSO - UniversityNiceSophiaAntipolis

Acknowledgments
IwouldliketoexpressmysincerelythankstomysupervisorsFabienGandonandCatherine
Faron-Zucker for their great help, support, inspire and advice. I was very lucky to have
themasmysupervisorssincetheysupportedmenotonlyontheresearch,butalsoonmany
aspectsofmylife.
I would also like to thank the rest of my thesis committee for their precious time,
insightful comments and helpful suggestions. I could not finish my thesis without all the
help.
IwouldliketothanktheOctopusproject(ANR-12-CORD-0026)forthefinancialsup-
portofmyresearch. Iwouldalsoliketothankourprojectpartnersforallthecollaborations
andmeetings. Itwasagreatpleasuretoworkwiththem.
I would also to thank the SMILK project for the financial support of my research and
thechanceofapplyingmyworkonadditionalreal-worlddataset.
I would like to thank Stack Overflow, Flickr and Viseo for sharing their data which
providedmewiththechancetoconductmyresearchproject.
IwouldliketothanktheWimmicsteam. Itisafriendlyandinternationalenvironment
whereIspentagreattimewithallmycolleagues.
IwouldliketothankChristineFoggiaforallherhelpandsupport.
Iwouldliketothankmydearfriendswhoalwayssupportedmeandencouragedme. I
alsowanttothankSophieforherhelpandsupport. Especially,IwouldliketothankFuqi
Songforhelpingmetogetovermanydifficulttimes. Iamsoluckytohaveallofthem.
Iwouldliketothankshanshanforherloveandsupport. Iamhappyandluckytohave
herwithme.
Iwouldliketoexpressmydeeploveandthankstomyfamilyfortheirsupportingand
understandingwheneverandwherever.

iii
Abstract
We propose an approach to detect topics, overlapping communities of interest, expertise,
trendsandactivitiesinuser-generatedcontentsitesandinparticularinquestion-answering
forumssuchasStackOverflow. WefirstdescribeQASM(Question&AnswerSocialMe-
dia), a system based on social network analysis to manage the two main resources in
question-answering sites: users and content. We also introduce the QASM vocabulary
used to formalize both the level of interest and the expertise of users on topics. We then
proposeanefficientapproachtodetectcommunitiesofinterest. Itreliesonanothermethod
to enrich questions with a more general tag when needed. We compared three detection
methods on a dataset extracted from the popular Q&A site StackOverflow. Our method
based on topic modeling and user membership assignment is shown to be much simpler
andfasterwhilepreservingthequalityofdetection. Wethenproposeanadditionalmethod
to automatically generate a label for a detected topic by analyzing the meaning and links
of its bag of words. We conduct a user study to compare different algorithms to choose a
label. Finally we extend our probabilistic graphical model to jointly model topics, exper-
tise, activities and trends. We performed experiments with real-world data to confirm the
effectivenessofourjointmodel,studyinguserbehaviorsandtopicdynamics.
Keywords:
socialsemanticweb, socialmediamining, probabilisticgraphicalmodel, questionan-
swer sites, user-generated content, topic modeling, expertise detection, overlapping com-
munitydetection

v
Résumé
Nousproposonsuneapprochepourdétecterlessujets,lescommunautésd’intérêtnondis-
jointes,l’expertise,lestendancesetlesactivitésdansdessitesoùlecontenuestgénérépar
lesutilisateursetenparticulierdansdesforumsdequestions-réponsestelsqueStackOver-
Flow. Nous décrivons d’abord QASM (Questions & Réponses dans des médias sociaux),
unsystèmebasésurl’analysederéseauxsociauxpourgérerlesdeuxprincipalesressources
d’unsitedequestions-réponses: lesutilisateursetlecontenu. Nousprésentonségalement
le vocabulaire QASM utilisé pour formaliser à la fois le niveau d’intérêt et l’expertise
des utilisateurs. Nous proposons ensuite une approche efficace pour détecter les commu-
nautésd’intérêts. Ellereposesuruneautreméthodepourenrichirlesquestionsavecuntag
plus général en cas de besoin. Nous comparons trois méthodes de détection sur un jeu de
donnéesextraitdusitepopulaireStackOverflow. Notreméthodebaséesurleserévèleêtre
beaucoupplussimpleetplusrapide,toutenpréservantlaqualitédeladétection. Nouspro-
posonsencomplémentuneméthodepourgénérerautomatiquementunlabelpourunsujet
détecté en analysant le sens et les liens de ses mots-clefs. Nous menons alors une étude
pour comparer différents algorithmes pour générer ce label. Enfin, nous étendons notre
modèle de graphes probabilistes pour modéliser conjointement les sujets, l’expertise, les
activitésetlestendances. Nouslevalidonssurdesdonnéesdumonderéelpourconfirmer
l’efficacité de notre modèle intégrant les comportements des utilisateurs et la dynamique
dessujets.
Mot-clés:
web social sémantique, l’analyse des médias sociaux, modèle graphique probabiliste,
sitesdequestions-réponses,contenugénéréparl’utilisateur,modélisationdesthématiques,
détectiond’expertise,ladétectiondecommunautésrecouvrantes

vii
You can’t connect the dots looking forward, you can only connect them looking back-
wards. So you have to trust that the dots will somehow connect in your future. –Jobs