72677 - Analysis of Social Networks Applied to Internet

Learning outcomes

People credited with this course know the theoretical and practical SNA - Social Network Analysis framework (concepts about network components and structure, network statistic, network representational power, analysis design, data handling and operational tools) applied to the Internet, in order to explore the most important topological, social and economic properties of the Net, in a big data perspective. That implies to acquire some practical skills in node-ties identification; data identification and collection; dataset building and data handling; measurements choice, carrying out and interpretation; graphical representation by the most common statistical net-packages.

Course contents

• Internet topology: small words, power law distributions, scale-free networks. Resiliency properties.
• SNA – Social Network Analysis foundations. Network as a complexity model.
• Internet (and big data) SNA. Applications in economics, social and computer sciences fields. Links with KM. Case studies.
• Network data: nodes and ties representations. One-mode and two-mode networks. Ego-centered networks. Directed, signed and valued graphs. Adiacency and incidency matrices. Matrices algebraic operations and their meanings. Blockmodel and image matrices. Nodes attributes.
• SNA packages. Network graphic representations: features, limits, domains.
• Network analysis design in the Internet: problem modelling, data identification and collection, dataset building and data handling, measurements choice, validity and interpretation. Bimodal dataset use in the Internet.
• Network graphical representations: theoretical layouts. Cognitive bias of interpretation.
• Descriptive network statistic. Nodal degree: in e out degree, density, reachability, connectivity, geodetic distance, diameter, maximum flow, reciprocity, transitivity and simmelian ties, clustering coefficent, External-Internal index.
• Centrality concept. Relations between centrality and power. Centrality measures: degree centrality (Freeman and Bonacich measures), closeness centrality, betweenness centrality.
• Egonet: definition and measurement. Structural holes: Burt theory. Brokerage: Fernandez and Gould measures.
• Groups and subgroups. Cliques, N-cliques, N-clans, K-cores, F-groups. Principal components analysis. Cutpoints and bridges.
• Outlines of role and position concepts. Similarity/Dissimilarity. Structural, automorphic and regular equivalence. Outlines of cluster analysis and factor analysis applied on similarity measures.

Readings/Bibliography

Teacher lecture notes. Network dataset for training use.

General bibliography
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.
Wasserman S. and Faust K. (1996), Social Network Analysis. Method and Applications, Cambridge (MA), Cambridge University Press.
Barabasi A-L. (2003), Linked: How Everything Is Connected to Everything Else and What It Means, Cambridge (MA), A plume book.

Teaching methods

Frontal teaching alternated with classroom exercices in the Internet

Assessment methods

Individual or small group project work, negotiated with the teacher

Teaching tools

Assisted case studies applying several statistical net-packages (Ucinet 6.90, Pajek 1.02, Netdraw 2.12, Mage 6.02, Gephi 0.8). Real applications design.

Office hours

See the website of Marco Ruffino