SOCIAL NETWORK ANALYSIS

Ingegneria Informatica SOCIAL NETWORK ANALYSIS

0622700060
DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
EQF7
COMPUTER ENGINEERING
2022/2023



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2017
SPRING SEMESTER
CFUHOURSACTIVITY
324LESSONS
324EXERCISES
Objectives
This course aims to provide the tools to better understand and control the processes that occur within a network. In particular, the course aims to provide a
framework, as comprehensive as possible, of recent research on the study and analysis of networks, with particular attention to social and information networks. The focus will not be on the technological aspects but, rather, on the understanding of the processes that develop within a network and on the techniques that can be used to extract knowledge from the structural analysis of the network.

Knowledge and understanding
Knowledge of the structural aspects that characterize a network and of the models and indicators that make it possible to effectively represent these networks and the behavior of their components.
Knowledge of the main processes and applications that can be performed on such networks, such as the search for information through search engines, sponsored research, the network effects in determining the behavior of network agents, the dissemination of information and influence and the effects that this can have on the structure of the network itself.

Applying knowledge and understanding
At the end of the course, students will be able to extract information related to networked processes, even from large datasets. They will also be able to analyze,
understand and control the main processes that develop within a network and apply this knowledge in the creation and management of applications that have social and economic components and in designing applications for social networks such as Facebook or Twitter.
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

DIDACTIC UNIT 1: NETWORK SCIENCE AND GRAPH THEORY
(LECTURE/PRACTICE/LABORATORY HOURS 9/0/6)
- 1 (2 HOURS LECTURE): INTRODUCTION TO NETWORK SCIENCE
- 2 (3 HOURS LECTURE): GRAPHS AND NETWORKS
- 3 (2 HOURS LECTURE): THE STRENGTH OF WEAK TIES
- 4 (3 HOURS LABORATORY): SOCIAL NETWORKS MINING: DIAMETER AND TRIANGLES
- 5 (3 HOURS LABORATORY): SOCIAL NETWORKS MINING: CLUSTERING AND CENTRALITY
- 6 (2 HOURS LECTURE): INFORMATION NETWORKS AND THE STRUCTURE OF THE WEB; ; WEB SEARCHING ALGORITHMS
KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE MAIN TOPOLOGICAL PROPERTIES OF INFORMATION AND SOCIAL NETWORKS AND THE MAIN MEASURES FOR QUANTIFYING THEM
APPLYING KNOWLEDGE AND UNDERSTANDING: DESIGN EFFICIENT ALGORITHMS FOR ANALYZING THE TOPOLOGICAL PROPERTIES OF SOCIAL NETWORKS

DIDACTIC UNIT 2: GAME THEORY AND MECHANISM DESIGN
(LECTURE/PRACTICE/LABORATORY HOURS 6/0/0)
- 7 (3 HOURS LECTURE): GAME THEORY
- 8 (3 HOURS LECTURE): AUCTION, MATCHING & SPONSORED SEARCH
KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE KEY CONCEPTS OF GAME THEORY: PLAYERS, STRATEGIES, EQUILIBRIA, TRUTHFULNESS
APPLYING KNOWLEDGE AND UNDERSTANDING: ABILITY IN FINDING EFFICIENT EQUILIBRIA AND MECHANISMS IN SETTINGS THAT HAVE NOT BEEN SEEN DURING LECTURES

DIDACTIC UNIT 3: BANDIT LEARNING
(LECTURE/PRACTICE/LABORATORY HOURS 4/0/3)
- 9 (2 HOURS LECTURE): INTRODUCTION TO BANDIT LEARNING
- 10 (2 HOURS LECTURE): COMBINATORIAL MULTI-ARM BANDIT LEARNING
- 11 (3 HOURS LABORATORY): BANDIT LEARNING
KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE KEY CONCEPTS AND ALGORITHMS OF BANDIT LEARNING
APPLYING KNOWLEDGE AND UNDERSTANDING: BEING ABLE TO APPLY EFFICIENT BANDIT LEARNING ALGORITHMS IN MULTIPLE SETTINGS

DIDACTIC UNIT 4: THE EFFECTS OF STRATEGIC BEHAVIORS IN A SOCIAL NETWORK
(LECTURE/PRACTICE/LABORATORY HOURS 9/0/3)
- 12 (2 HOURS LECTURE): NETWORK EFFECTS
- 13 (3 HOURS LECTURE): INFORMATION CASCADES
- 14 (2 HOURS LECTURE): RICH GET RICHER PHENOMENON AND POWER LAWS
- 15 (2 HOURS LECTURE): SMALLWORLDS
- 16 (3 HOURS LABORATORY): NETWORK MODELS
KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING HOW THE STRATEGIC BEHAVIOR OF USERS MAY INFLUENCE THE NETWORK TOPOLOGY AND THE INFORMATION SPREADING OVER THIS NETWORK
APPLYING KNOWLEDGE AND UNDERSTANDING: BEING ABLES OF PRODUCE MODELS THAT MAY EFFICIENTLY AND EFFECTIVELY SIMULATE REAL SOCIAL NETWORKS

DIDACTIC UNIT 4: INFORMATION DIFFUSION ON SOCIAL NETWORKS
(LECTURE/PRACTICE/LABORATORY HOURS 5/0/3)
- 17 (3 HOURS LECTURE): INFORMATION DIFFUSION AND EPIDEMICS
- 18 (3 HOURS LABORATORY): DYNAMICS & SEEDING
- 19 (2 HOURS LECTURE): ELECTION MANIPULATION
KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE KEY CONCEPTS OF THE INFORMATION DIFFUSION ON SOCIAL NETWORKS, AND THE MAIN TECHNIQUES USED FOR INCREASING OR CONTRASTING THIS DIFFUSION
APPLYING KNOWLEDGE AND UNDERSTANDING: BEING ABLE TO PRODUCE DIFFUSION MODELS THAT MAY EFFECTIVELY SIMULATE DIFFUSION ON REAL SOCIAL NETWORKS, AND TO DESIGN EFFICIENT ALGORITHMS FOR INCREASING OR CONTRASTING THIS DIFFUSION


TOTAL LECTURE/PRACTICE/LABORATORY HOURS 33/0/15
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 IS HELD IN ITALIAN

THE TEACHING MATERIAL IS AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS.
  BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2022-11-21]