PERIOD: JANUARY - FEBRUARY In this course, we shall explore data analysis and prediction topics using complex network datasets. In particular, our focus will be on temporal and structural data. Examples of datasets that will be explored can be found on the SNAP [1], ICON [2] and Koblenz [3] network repositories. [1] https://snap.stanford.edu/data/ [2] https://icon.colorado.edu/#!/networks [3] https://konect.uni-koblenz.de/

AULA C - dalle 14:00 alle 17:00 Jan 15 Intro to network science Fabricio/Flavio Jan 16 Probabilistic network models Fabricio Jan 17 Link prediction Fabricio Jan 22 Random walks Fabricio Jan 23 Graph node embeddings Fabricio Jan 24 Network analysis using n-dimensional data Flavio Jan 29 Cascades and time series Flavio Jan 30 Point processes on networks Flavio Jan 31 Granger causality and covariance extraction Flavio Feb 05 Project presentation Fabricio/Flavio

Fabricio Murai Ferreira (born 20/11/1985) is an Assistant Professor in the Department of Computer Science at the Universidade Federal de Minas Gerais, Brazil. His research lies in the application of mathematical modeling, statistics and machine learning to computer, informational and social networks. In particular, his work focuses on partially observed networks. He obtained his B.Sc. degree (*magna cum laude*) from the Universidade Federal do Rio de Janeiro in 2007 and his Ph.D. degree under Professor Don Towsley from the University of Massachusetts Amherst in 2016, both in Computer Science. During his graduate studies, he received a number of awards including a 4-year scholarship from CNPq (Brazil's National Research Council) and a 2013 UMass Amherst CS Outstanding Synthesis Award sponsored by Yahoo!. He has published in top scientific venues such as IEEE Journal of Selected Areas in Communications, Data Mining and Knowledge Discovery, IEEE INFOCOM, IEEE/ACM ASONAM etc. He serves as a TPC member for the IEEE INFOCOM and the IEEE/WIC/ACM Web Intelligence and has reviewed papers for important journals in his field. MURAI, FABRICIO; RIBEIRO, BRUNO ; TOWSLEY, DON ; WANG, PINGHUI . On Set Size Distribution Estimation and the Characterization of Large Networks via Sampling. IEEE Journal on Selected Areas in Communications (Print), v. 31, p. 1017-1025, 2013. MURAI, FABRICIO; ROCHA, ANTONIO A. DE A. ; FIGUEIREDO, DANIEL R. ; DE SOUZA E SILVA, EDMUNDO A. . Heterogeneous download times in a homogeneous BitTorrent swarm. Computer Networks (1999), v. 56, p. 1983-2000, 2012. MURAI, FABRICIO; RENNÓ, DIOGO ; RIBEIRO, BRUNO ; PAPPA, GISELE L. ; TOWSLEY, DON ; GILE, KRISTA . Selective harvesting over networks. DATA MINING AND KNOWLEDGE DISCOVERY (DORDRECHT. ONLINE), v. 32, p. 187-217, 2017. Flavio Figueiredo (born 30/09/1985) is a professor at Universidade Federal de Minas Gerais (UFMG). He received his PhD and MSc degrees from the same university and his BSc from Universidade Federal de Campina Grande (UFCG). In the past, he was a visiting scholar at Carnegie Mellon University as well as the at the University of British Columbia. Flavio has also worked on industry research for a year at IBM's Research Lab in Rio de Janeiro. Currently, he performs research developing and applying data science and machine learning algorithms for a wide range of contexts (from social media, Internet traces and cultural production). In particular, he is interested in understanding large scale social and cultural phenomena using online data. Flavio has published papers in prestigious conferences such as CHI, WWW, WSDM, ECML/PKDD, WebSci, and ISMIR. Figueiredo, Flavio; ALMEIDA, JUSSARA M. ; GONÇALVES, MARCOS ANDRÉ ; BENEVENUTO, FABRÍCIO . On the Dynamics of Social Media Popularity. ACM Transactions on Internet Technology, v. 14, p. 1-23, 2014. Almeida, Jussara ; Goncalves, Marcos Andre ; Figueiredo, Flavio ; Pinto, Henrique ; Belem, Fabiano . On the Quality of Information for Web 2.0 Services. IEEE Internet Computing, v. 14, p. 47-55, 2010. Figueiredo, Flavio; Pinto, Henrique ; BELÉM, FABIANO ; Almeida, Jussara ; GONÇALVES, MARCOS ; FERNANDES, DAVID ; MOURA, EDLENO . Assessing the quality of textual features in social media. Information Processing & Management, v. 49, p. 222-247, 2013.

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Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.