Corso di Dottorato “Advanced Learning and Mining Problems in Big Graph Data”, A.Tagarelli – F.Gullo – Inizio 13/02/2018

TITLE:   Advanced Learning and Mining Problems in Big Graph Data
This course is an introduction to computational problems and relating solutions for the analysis of big graphs and information networks, with a focus on machine learning and data mining methods. The aim is to give students an overview of research questions and applications connected to different types of information networks, including social networks, location-aware networks, rating networks, semantic networks.
This course is conceived as organized into two parts. The first part provides an introduction to classic models and algorithms designed to analyze information networks, including topological characterization of networks, network models, centrality and ranking methods, information diffusion models, influence propagation methods. The second part is focused on “feature-rich” (or complex) networks (such as, multilayer  graphs, time-evolving graphs, uncertain or probabilistic graphs) and on challenging/emerging problems defined upon such networks, including multilayer community detection, dense-subgraph extraction, community search and evolution, network filtering and decomposition.
* Andrea Tagarelli, DIMES
* Francesco Gullo, UniCredit Ricerca & Sviluppo
Andrea Tagarelli is associate professor of computer engineering at the University of Calabria, Italy. He graduated magna cum laude in computer engineering, in 2001, and obtained his Ph.D. in computer and systems engineering, in 2006. In 2017, he obtained the Italian national scientific qualification to full professor, for the computer science and engineering research area, scientific disciplinary sector (SSD) ING-INF/05 “Sistemi di Elaborazione delle Informazioni”. His research interests include topics in data mining, web and network science, information retrieval, machine learning, artificial intelligence. On these topics, he has coauthored more than 90 peer-reviewed papers, including journal articles, conference papers and book chapters. He also edited a book titled “XML Data Mining: Models, Methods, and Applications”. He was co-organizer of workshops and a mini-symposium on data mining topics in premier conferences in the field (ACM SIGKDD, SIAM DM, PAKDD, ECML-PKDD, ECIR). He has also served as a reviewer as well as a member of program committee for leading journals and conferences in the fields of databases and data mining, knowledge and data engineering, network analysis, information systems, knowledge based systems, and artificial intelligence. Since 2015, he is in the editorial board of Computational Intelligence Journal and Social Network Analysis and Mining Journal.
He is PC co-chair of the 2018 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining.
Francesco Gullo is researcher at UniCredit, R&D department. He received his Ph.D. from the University of Calabria (Italy) in January 2010. During his Ph.D., in 2009, he was an intern at the George Mason University, Fairfax VA, USA. From January 2010 to August 2011 he was a postdoctoral research fellow at the University of Calabria. Before joining UniCredit, he spent four years (from September 2011 to June 2015) in Barcelona, working at Yahoo Labs, first as a postdoctoral researcher, and, starting from September 2013, as a research scientist.
His research interests fall into the broad area of data science, with special emphasis on data mining, machine learning, and databases. His work is centered on real-world problems that require large-scale data processing, and he usually tackles such problems from a combinatorial-optimization and algorithmic perspectives. Specifically, his current research is mainly devoted to graph analytics, graph miningand graph querying, as well as (social) Web mining, and NLP, while in the past he has also focused on data mining/machine learning for high-dimensional/multi-faceted data, uncertain/probabilistic data, spatio-temporal data, biological data, and XML. His research has been published in premier venues such as SIGMOD, VLDB, KDD, ICDM, CIKM, EDBT, WSDM, ECML PKDD, SDM, TODS, TKDD, Machine Learning, DAMI, JCSS, Pattern Recognition. He has also been active in serving the datamining/databases scientific community, by, e.g., being Workshop Chair of ICDM’16, organizing workshops/symposia (MIDAS @ECML-PKDD[’16,’17], MultiClust 2014 mini-symposium @SDM’14, 4th MultiClust workshop @KDD’13, 3Clust workshop @PAKDD’12), or being part of the program committee of major conferences (KDD, WWW, ICDM, WSDM, ECML-PKDD, CIKM, ICWSM, SDM).
Martedì 13 febbraio, ore 15.00-18:00
Mercoledì 14 febbraio, ore 15.00-18:00
Giovedì 15 febbraio, ore 15.00-18:00
Venerdì 16 febbraio, ore 10.00-13:00Aula seminari,  cubo 44, piano terra.