90730 - SOCIAL NETWORK ANALYSIS

Learning outcomes

At the end of the course, students gain knowledge on the Web as a socio-technical system involving specific processes, entities, and behaviours, using interdisciplinary methods that blend computer science, sociology, ethnography, economics, linguistics, etc. The students are able to analyse the Web phenomena similarly to typical objects from natural sciences, distinguishing between data and applications, agents from computationally generated profiles, and addressing the characteristics of networks of entities emerging from the informationl, physical, social, and conceptual spaces constituting the Web.

Course contents

The programme is the same for both attending and non-attending students:

# Introduction to Network Analysis, gentle introduction to the field of network analysis and its usages in other fields of research (e.g., computer science, forensics, archeology, literature, history, science of religion, etc.).

# Research Design and How to Read a (Network Analysis) Research Paper: introduction to the scientific publication process, elements of research papers (on network analysis), research design, analysis of research papers.

# Mathematics of Networks: networks and their representation, types of networks, graph representations, paths and components, adjacency matrices and matrix representations, ways and modes, operations on Matrices.

# Data Collection and Data Management: network questions, data collection and reliability, data formats and transformation, algorithms and software for network analysis and visualisation.

# Measures and Metrics, Nodes: kinds of measures, multi-category nominal scales, ordinal and scalar measures, centrality, degree and other kinds of centrality (e.g. Google's PageRank), hubs and authorities, closeness and betweenness centrality, groups of nodes (cliques, cores, components and k-components), clustering and clustering coefficients, reciprocity and similarity, structural and other types of equivalence, homophily and types of assortative mixing.

# Testing Hypotheses: the role of hypotheses in the scientific method, testing hypotheses in network analysis, permutation tests, dyadic hypotheses.

# Ego Networks: ego networks, obtaining and analysing ego-network data, tie analysis, structural-shape measures.

# Measures and Metrics, Networks: small-world effects, degree distribution, power laws and scale-free networks, visualisation and properties of power-law distributions, local-clustering coefficient, cohesion, reciprocity, transitivity and the clustering coefficient, triad census, centralisation and core-periphery indices, centrality, random graphs, means on edges and degree, degree distribution, giant and small component(s), locally tree-like networks.

# Dividing Networks into Groups: dividing networks into groups, modularity maximisation, methods based on information theory, methods based on statistical inference, betweenness-based methods (dendrograms), hierarchical clustering, overlapping communities, hierarchical communities, latent spaces, stratified networks, and rank structure, percolation and network resilience, uniform and non-uniform (random) node removal.

# Network Visualisation: the importance of network visualisation, graph-layout algorithms, embedding node attributes, node filtering, visualising ego networks, embedding tie characteristics, tie strengths, visualising network change.

# Handling Large Networks: reducing the size of the problem, eliminating edges, pruning nodes, divide and conquer, aggregation, sampling, small-world and scale-free networks.

Readings/Bibliography

Lecture Notes, Research Papers, and datasets provided by the teacher.

Main references:

Newman, M. (2018). Networks. Oxford university press. Recommended chapters: 1 (+ one or more among 2, 3, 4, 5), 6, 7, 10, 11.

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Sage. Recommended chapters: 1–8.

Additional references:

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.

Watts, D. J. (2004). Six degrees: The science of a connected age. WW Norton & Company.

Koch, R., & Lockwood, G. (2011). Superconnect: Harnessing the power of networks and the strength of weak links. Random House Digital, Inc..

Barabasi, A. L. (2014). Linked-how Everything is Connected to Everything Else and what it Means F (pp. 1-1). Perseus Books Group.

Teaching methods

Frontal teaching and students seminars/flipped classrooms.

Assessment methods

The exam’s hand-in consists of a report: a 5-6 pages PDF document (8-9 pages for projects with more students), detailing the context, the problem/motivation, the data, the measures, and the results of the project that the students performed.

The report is the artefact evaluated for the exam and it is an essential tool for the students to check that they considered and reported on all the important aspects of their project study. The clear presentation/explanation of those aspects also forms the weighted partition that guides the grading, as listed below:

• context, problem(s), motivation — 20%
• dataset(s) and source(s), validity and reliability,
  applied measure/s and its/their justification, possible hypotheses and tests, results — 60%
• interpretation of the overall results, critique (brief) — 20%

Please, refer to the website of the teacher for the exam description.

Teaching tools

Network analysis software/packages (Ucinet, Gephi, Cytoscape, Pajek, NodeXL) to analyse case studies, applying statistical measures and performing network visualisations.

Links to further information

https://saveriogiallorenzo.com/teaching#na

Office hours

See the website of Saverio Giallorenzo