SOCIAL NETWORK ANALYSIS

Diodato FERRAIOLI SOCIAL NETWORK ANALYSIS

0622700060
DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
EQF7
COMPUTER ENGINEERING
2019/2020



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2017
SECONDO SEMESTRE
CFUHOURSACTIVITY
324LESSONS
324EXERCISES
Objectives
THE YEARS FOLLOWING THE EMERGENCE OF THE WEB CAN BE SEEN AS THE YEARS OF THE SOCIAL COMPUTING. SEVERAL SOCIAL APPLICATIONS APPEARED, SUCH AS FACEBOOK AND TWITTER. TECHNOLOGICAL AND ECONOMICAL SYSTEMS WE USE IN OUR EVERYDAY LIFE ARE BASED ON EXTREMELY COMPLEX NETWORKS AND IT IS GETTING MORE AND MORE DIFFICULT TO UNDERSTAND HOW THESE SYSTEMS WORK . TO DEAL WITH THESE NEW SYSTEMS WE HAVE TO CHANGE OUR PARADIGM AND LEARN TO REASON ABOUT AN HIGHLY INTERCONNECTED WORLD. IT IS OF FUNDAMENTAL IMPORTANCE TO BE ABLE TO UNDERSTAND HOW THESE NETWORKS WORK AND RECOGNIZE AND CONTROL THE PROCESSES THAT DEVELOP IN A NETWORK. THIS COURSE AIMS TO GIVE STUDENTS THE TOOLS TO BETTER UNDERSTAND AND CONTROL HIGHLY CONNECTED NETWORKS, WITH PARTICULAR EMPHASIS TO SOCIAL AND INFORMATION NETWORKS TECHNOLOGICALLY MEDIATED..


KNOWLEDGE AND UNDERSTANDING

DURING THE COURSE WE WILL FIRST DESCRIBE MODELS TO EFFICENTLY REPRESENT NETWORKS AND TECHNIQUES TO AUTOMATICALLY EXTRACT INFORMATION ON THE NETWORK FROM THEIR STRUCTURE. THEN, WE WILL DESCRIBE NETWORK PROCESSES SUCH AS WEB SEARCHING AND THE ROLE OS THE SEARCH ENGINES, ONLINE ADVERTISEMENT AND AUCTIONS, SPREAD OF INFORMATION AND/OR INNOVATION IN A NETWORK, CASCADING BEHAVIOURS, SYSTEMS TO AGGREGATE OPINIONS AND MAKE COLLECT DECISIONS, VOTING.
THE MAIN TOOLS USED IN THIS COURSE ARE GRAPH THEORY TO DESCRIBE AND ANALIZE THE STRUCTURE OF THE NETWORKS AND GAME THEORY TO MODEL THE STRATEGIC BEHAVIOURS OF THE AGENTS. A PART OF THE COURSE WILL BE DEVOTED TO CODING IN PYTHON. IN THIS PART EXPERIMENTS WILL BE PROPOSED THAT, USING API AND DATASETS PUBLICLY AVAILABLE ON THE INTERNET, WILL ANALIZE THE STRUCTURE OF THE NETWORKS AND EXTRACT INFORMATION.

APPLYING KNOWLEDGE AND UNDERSTANDING

AT THE END OF THE COURSE STUDENTS WILL BE ABLE TO ANALIZE, UNDERSTAND AND DRIVE PROCESSES THAT DEVELOP INTO A SOCIAL CONTEXT, THEY WILL BE ABLE TO APPLY THEIR KNOWLEDGE TO DESIGN, DEVELOP AND ADMINISTRATE APPLICATIONS HAVING A SOCIAL COMPONENT. THEY WILL ALSO BE ABLE TO DESIGN APPLICATIONS FOR SICAL NETWORKS SUCH AS FACEBOOK, TWITTER ETC.
THEY WILL BE ABLE TO EXTRACT INFORMATION ON THE NATURE AND THE STRUCTURE OF A NETWORK EVEN FROM LARGE DATASETS.
Prerequisites
FOR THE SUCCESSFUL ACHIEVEMENT OF COURSE OBJECTIVES STUDENTS ARE REQUIRED TO HAVE GOOD COMPETENCE ON ALGORITHMS AND PROGRAMMING TECHNIQUES AND THE KNOWLEDGE OF THE PROGRAMMING LANGUAGE PYTHON. MOREOVER, IT IS ALSO REQUIRED BASIC COMPETENCE IN PROBABILITY AND LINEAR ALGEBRA.
Contents
THE MAIN TOOLS USED IN THIS COURSE ARE GRAPH THEORY TO DESCRIBE AND ANALIZE THE STRUCTURE OF THE NETWORKS AND GAME THEORY TO MODEL THE STRATEGIC BEHAVIOURS OF THE AGENTS. A PART OF THE COURSE WILL BE DEVOTED TO CODING IN PYTHON. IN THIS PART EXPERIMENTS WILL BE PROPOSED THAT, USING API AND DATASETS PUBLICLY AVAILABLE ON THE INTERNET, WILL ANALIZE THE STRUCTURE OF THE NETWORKS AND EXTRACT INFORMATION.

MAIN ARGUMENTS COVERED IN THE COURSE

NETWORKS
- ROLE OF THE NETWORKS IN THE MODERN SOCIETY.
- NETWORKS REPRESENTED AS GRAPHS.
- STRUCTURAL ANALYSIS OF NETWORKS.
LECTURE HOURS: 6

GAME THEORY
DEFINITION OF A GAME AND ITS REPRESENTATION IN NORMAL OR EXTENSIVE FORM.
PURE AND MIXED STRATEGIES.
SOLUTION CONCEPTS (DOMINANT STRATEGIES, NASH EQUILIBRIA, CORRELATED EQUILIBRIA).
CONGESTION GAMES.
EVOLUTIONARY GAME THEORY
AUCTIONS.
LECTURE HOURS: 8

WEB SEARCHING AND SPONSORED SEARCH
- THE STRUCTURE OF THE WEB.
- WEB SEARCHING THROUGH LINK ANALYSIS (HITS AND PAGERANK).
SEARCH ENGINES.
SPONSORED SEARCH, TRUTHFUL MECHANISMS AND VCG MECHANISMS.
AUCTIONS: SECOND PRICE AND GENERALIZED SECOND PRICE AUCTIONS.
LECTURE HOURS: 8

NETWORK DYNAMICS AND SPREAD OF INFORMATION
CASCADING BEHAVIOURS AND NETWORK EFFECTS.
POWER LAWS AND RICH-GET-RICHER PHENOMENON.
SMALL-WORLDS.
EPIDEMICS.
RECOMENDATION AND REPUTATIONS SYSTEMS.
LECTURE HOURS: 10

LABORATORY ACTIVITY – PROGRAMMING IN PYTHON/XML
EXPERIMENTS ON SEVERAL PUBLIC DATASETS.
EXTRACTION OF INFORMATION FROM THE ANALYSIS OF THE STRUCTURE OF THE NETWORK.
LECTURE HOURS: 16

Teaching Methods
THE COURSE CONSISTS OF LECTURES AND GUIDED ACTIVITIES IN LAB REQUIRING PROGRAMMING IN PYTHON.

DURING THE LECTURES MODELS ARE PRESENTED TO REPRESENT SOCIAL NETWORKS AND DESCRIBE GLOBAL PHENOMENA OCCURING IN THE NETWORK IN TERMS OF THE LOCAL BEHAVIOURS OF AGENTS AND ALGORITHMS ARE PRESENTED TO DESCRIBE SUCH PROCESSES AND EXTRACT INFORMATION FROM NETWORKS OF LARGE SIZE.
IN THE LAB STUDENTS ARE REQUIRED TO IMPLEMENT ALGORITHMS PRESENTED IN THE LECTURES.
IN THE GUIDED EXERCISES STUDENTS ARE DIVIDED IN GROUPS AND EACH GROUP IS ASSIGNED PROJECT-WORKS TO DEVELOP DURING THE WHOLE COURSE. THE PROJECT INCLUDES ALL THE MATERIAL OF THE COURSE AND IS FINALIZED TO THE ACQUISITION OF THE CAPACITY TO ANALYZE SOCILA NETWORKS OF LARGE SIZE AND EXTRACT INFORMATIONS FROM MASSIVE DATA SETS.MOREOVER, THE PROJECT-WORK IS ALSO USED TO DEVELOP THE ABILITY OF WORKING IN A TEAM.
Verification of learning
THE FINAL EXAM IS DESIGNED TO EVALUATE AS A WHOLE THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, AND THE ABILITY TO APPLY SUCH KNOWLEDGE IN THE CONTEXTS OF NETWORKS SCIENCE TO ANALYZE PROCESSES RUNNING IN SOCIAL AND INFORMATION NETWORKS.
THE EXAM CONSISTS OF THE DISCUSSION OF A GROUP-PROJECT REALIZED DURING THE COURSE AND AN ORAL INTERVIEW. THE PROJECT AIMS TO ASSESS THE ABILITY OF APPLYING KNOWLEDGE IN ANALYZING AND EXTRACTING INFORMATION FROM REAL NETWORKS AND DESGNING SOCIAL APPLICATIONS. IT ALSO ASSESSES THE CAPACITY OF WORKING IN GROUP AND THE PRESENTATION SKILLS. THE INTERVIEW AIMS TO ASSESS THE ACQUIRED KNOWLEDGE AND UNDERSTANDING OF MODELS AND TECHNIQUES USED TO STUDY SOCIAL NETWORKS.
IN THE FINAL EVALUATION, EXPRESSED IN THIRTIES, THE EVALUATION OF THE PROJECT WILL ACCOUNTS FOR 60% WHILE THE INTERVIEW FOR THE REMAINING 40%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE.
Texts
D. EASLEY, J. KLEINBERG, “NETWORKS, CROWDS AND MARKETS: REASONING ABOUT A HIGHLY CONNECTED WORLD”, CAMBRIDGE UNIVERSITY PRESS, 2010.

J.LESKOVEC, A. RAJAMARAN, J. ULLMAN, “MINING OF MASSIVE DATASETS”, CAMBRIDGE UNIVERSITY PRESS, 2014.

OTHE MATERIAL WILL BE MADE AVAILABLE ON THE COMPANION WEB SITE..

SUGGESTED READINGS:
M. JACKSON, “SOCIAL AND ECONOMIC NETWORKS”, PRINCETON UNIVERSITY PRESS, 2010.
M.E.J. NEWMAN, “NETWORKS: AN INTRODUCTION”, OXFORD UNIVERSITY PRESS, 2010.
N.NISAN, T. ROUGHGARDEN, E. TARDOS, V. VAZIRANI (A CURA DI), “ALGORITHMIC GAME THEORY”, CAMBRIDGE PRESS, 2007.
M. OSBORNE, A. RUBINSTEIN, “A COURSE IN GAME THEORY”, MIT PRESS, 1994.
More Information
THE COURSE LANGUAGE IS ITALIAN. TEXTS, SLIDES AND THE OTHER MATERIAL WILL BE PROVIDED ARE IN ENGLISH.

THE COURSE HAS A COMPANION WEB SITE PUBLISHED IN THE DIEM E-LEARNING PLATFORM (HTTP://ELEARNING.DIEM.UNISA.IT - CLASS DI RETI SOCIALI OF THE CDS MAGISTRALE IN INGEGNERIA INFORMATICA) THAT CAN BE ACCESSED BY ALL THE ENGINEERING STUDENTS AT UNISA. ON THE SITE YOU CAN FIND ANNOUNCEMENTS, NEWS, A FORUM FOR DISCUSSIONS RELATED TO THE COURSE, DIDACTIC MATERIAL, SLIDES, EXERCISES AND EXAMS, LECTURE PLAN AND A SURVEY OF THE ARGUMENTS OF EACH LECTURE. TO ACCESS THE SITE YOU NEED TO REGISTER.
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