72677 - Analysis of Social Networks Applied to Internet

Course contents

Introduction. Fundamental Concepts of Network Analysis. Examples. Background.
Relationship Analysis. Fundamental concepts in SNA. Structural and composition variables. Affiliation Variables. Types of Networks. Measurement Levels. One-Mode Networks, Two-Mode Networks. Ego-centered networks.
Data collection and measurements. Validation of measures, reliability, accuracy, error. Small World.
Some network datasets.
Mathematical representation of social networks. Representation of knots and bonds. Sociometric and Algebraic Notation. Graphs and Matrices. Algebraic operations on matrices and their meaning. Calculate Simple Network Properties.
Descriptive statistics of networks. Degree of the nodes: in-degree and out-degree, density, reachability, connectivity, geodetic distance, diameter, maximum flow, reciprocity, transitivity and Simmelian bonds, clustering coefficient, External-Internal index.
Structural and Localization Properties. Centrality and Prestige. Concept of centrality and its relations with the concept of power. Degree of centrality: Freeman and Bonacich measures. Betweenness centrality.
Groups and subgroups. Cliques, N-cliques, N-clans, K-cores, F-groups. Analysis of the main components. Break points and bridges.
Concepts of role and position outline. Similarity / Dissimilarity. Structural, automorphic and regular equivalence. Introduction to multivariate statistics techniques (cluster analysis, factor analysis) applied to similarity measures
Definition and measures of Egonet. Structural holes: Burt's theory. Brokerage: measures by Fernandez and Gould
The (S)NA applied to the Internet and to big data. Typical areas of work in the economic, social and IT fields. Relations with Knowledge Management. Case studies.
Analysis applications and graphical representation of networks: characteristics, limits, areas of use.
The design of a network analysis on the Internet: from the real problem to the model, identification of the data and their collection, construction of the dataset and operations on the data, classes of measures and their choice; validity of the results and their interpretation. Use of bimodal datasets on the Internet.

Readings/Bibliography

Lecture notes of the teacher
Datesets made available by the teacher for classroom exercises


READINGS/BIBLIOGRAPHY

Stanley Wasserman, Katherine Faust (1994), Social Network Analysis: Methods and Applications, Cambridge University Press.

Koch R. and Lockwood G. (2010), Superconnect. The Power of Networks and the Strenght of Weak Links, London, Little Brown.
Watts D. (2004), Small Worlds. The dynamicof Networks between Order and Randomness, Princeton, Princeton University Press.
Carrington P., Scott J. and Wasserman S., (2005), Models and Methods in Social Network Analysis, Cambridge (MA), Cambridge University Press.
Barabasi A-L. (2002), Link. La scienza delle reti, Torino, Einaudi.
Salvini A. (2007), Analisi delle reti sociali. Teorie, metodi, applicazioni, Milano, Franco Angeli.
Wasserman S. and Faust K. (1996), Social Network Analysis. Method and Applications, Cambridge (MA), Cambridge University Press.

Teaching methods

Traditional classes

Assessment methods

Final grades are determined by evaluating an individual work that each student must detail/propose in advance, toward a final examination in form of both (i) a written (paper-like) document, and (ii) a spoken presentation.

Teaching tools

https://networkx.github.io/

http://snap.stanford.edu/

Office hours

See the website of Andrea Piroddi