000225308 001__ 225308
000225308 005__ 20190619023713.0
000225308 0247_ $$2doi$$a10.5075/epfl-thesis-7428
000225308 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis7428-8
000225308 02471 $$2nebis$$a10817705
000225308 037__ $$aTHESIS
000225308 041__ $$aeng
000225308 088__ $$a7428
000225308 245__ $$aFrom recommender systems to spatio-temporal dynamics with network science
000225308 269__ $$a2017
000225308 260__ $$bEPFL$$c2017$$aLausanne
000225308 300__ $$a155
000225308 336__ $$aTheses
000225308 502__ $$aProf. Pascal Frossard (président) ; Prof. Pierre Vandergheynst (directeur de thèse) ; Dr Olivier Verscheure, Dr Shebli Anvar, Dr Michalis Vlachos (rapporteurs)
000225308 520__ $$aNetworks are data structures that are fundamental for capturing and analyzing complex interactions between objects. While they have been used for decades to solve problems in virtually all scientific fields, their usage for data analysis in real-world practical applications deserves to be further investigated.   In this thesis, we explore multiple aspects of network science and show how the design of new graph-based approaches offers an unprecedented depth for analyzing complex datasets. Through the study of practical applications, we demonstrate how to extract key findings in several domains such as digital humanities, recommender systems, social behavior, neuroscience or knowledge discovery.   First, we propose to define in a concise manner the data science workflow. We present the tools, techniques, and questions that the practitioner needs to have in mind when addressing a new large-scale problem as they are of tremendous importance if one wants to apply network science concepts to real applications.   Based on this foundation chapter, we begin by demonstrating the worth of networks for music recommendation with Genezik, our smart playlist application that adapts to user taste. Using signal processing, machine learning, and graphs, we show how to improve the performance of recommender systems as well as proposing a radically different user experience that has yet to be found in competing systems.   We then move on to the introduction of the causal multilayer graph of activity, a novel graph method dedicated to the analysis of dynamical processes over networks. More than a data structure, we present a data analysis approach that tracks spreading or propagation of events through time in a scalable manner by efficiently combining a network with values associated with its vertices. Used in four different applications, the analysis of spatio-temporal patterns of activity extracted from the causal multilayer graph helps us better understand how rumors spread in social networks or how brain regions interact in resting states for instance.   Finally, we study the browsing behavior of millions of people on Wikipedia and show how to extract contextual patterns of activity that reflect what is collectively remembered from past events. Based on their analysis, we confirm social studies on human behavior and conclude by revealing some of the rules that define human curiosity.
000225308 6531_ $$anetwork science
000225308 6531_ $$adata mining
000225308 6531_ $$adata analytics
000225308 6531_ $$aknowledge discovery
000225308 700__ $$0245769$$g204172$$aBenzi, Kirell Maël
000225308 720_2 $$aVandergheynst, Pierre$$edir.$$g120906$$0240428
000225308 8564_ $$uhttps://infoscience.epfl.ch/record/225308/files/EPFL_TH7428.pdf$$zn/a$$s26789306$$yn/a
000225308 909C0 $$xU10380$$0252392$$pLTS2
000225308 909CO $$pthesis-bn2018$$pthesis-public$$pDOI$$ooai:infoscience.tind.io:225308$$qGLOBAL_SET$$pSTI$$pthesis$$qDOI2
000225308 917Z8 $$x108898
000225308 917Z8 $$x108898
000225308 917Z8 $$x108898
000225308 918__ $$dEDEE$$cIEL$$aSTI
000225308 919__ $$aLTS2
000225308 920__ $$b2017$$a2017-1-31
000225308 970__ $$a7428/THESES
000225308 973__ $$sPUBLISHED$$aEPFL
000225308 980__ $$aTHESIS