90730 - SOCIAL NETWORK ANALYSIS

Learning outcomes

The course is meant to examine the essential models, methods and topics of social network analysis, while also comprehending information networks, where more generally nodes and links represent respectively data and relations between data. The first half is devoted to those analytical methods based on the comparison between real-world networks and random graphs, with emphasis on the configuration model and modularity clustering.The second half is devoted to objective function-based methods and to fuzzy models for community/module detection, with focus on the cluster score of vertex subsets quantified by pseudo-Boolean functions.

Course contents

# 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.

General Bibliography

Newman, M. (2018). Networks. Oxford university press.

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Sage.

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

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.

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

Teaching methods

Frontal teaching and students seminars/flipped classrooms.

Assessment methods

Project work, either individual or in a small group, negotiated with the teacher.

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/

Office hours

See the website of Saverio Giallorenzo