90730 - SOCIAL NETWORK ANALYSIS

Conoscenze e abilità da conseguire

L'obiettivo del corso è quello di esaminare i modelli, metodi e temi essenziali dell'analisi delle reti sociali, comprendendo anche le information networks, dove più in generale nodi e links rappresentano rispettivamente dati e relazioni fra dati. La prima parte è dedicata ai metodi d'analisi basati sul confronto fra reti reali e random graphs, con enfasi su configuration model e modularity clustering. La seconda parte è dedicata ai metodi objective function-based e ai modelli fuzzy per module/community detection, con focus sul cluster score dei sottoinsiemi di vertici quantificato da funzioni pseudo-Booleane.

Contenuti

# 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.

Testi/Bibliografia

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.

Metodi didattici

Frontal teaching and students seminars/flipped classrooms.

Modalità di verifica e valutazione dell'apprendimento

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

Refer to the website of the teacher for the exam description.

Strumenti a supporto della didattica

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

Link ad altre eventuali informazioni

https://saveriogiallorenzo.com/teaching/

Orario di ricevimento

Consulta il sito web di Saverio Giallorenzo