Network Optimization and Social Networks

Introductory knowledge of the Python language is recommended.

Students are requested to follow the classes onsite. Lecturing involves the exposition of concepts, techniques and methods, with a strong focus on practical applications. Adequate software will be used in each topic of the program.

Objectives:
A large part of the nowadays data generated have interaction relations between their elements, forming networks/graphs. The study of these networks and their structures brings very relevant information for its discussion, allowing the observation of properties and patterns in the relationships between their elements. These properties have long been studied in the scope of graph theory.
In this discipline, several of these properties are addressed, focusing on optimization techniques in networks and network analysis techniques. These techniques will be used for the study of road networks, trade flow networks, social networks and biological networks.

Competencies:
It is intended that the student knows the main characteristics and topological properties of a network/graph. It is also intended that the student knows techniques for the analysis of networks, involving paths, flows, centrality, community and grouping, with main motivation on social networks analysis.

1 – Networks/Graphs properties
   1.1 – Degree and incidence
   1.2 – Paths
   1.3 – Flows
   1.4 – Centrality
   1.5 – Covering and influence
   1.6 – Community
2 – Data structures for networks
3 – Tools for building and analysing networks
   3.1 – Gephi
   3.2 –NetworkX library from Python
4 – Study of applications resorting to network analysis
   4.1 – Road networks
   4.2 – Commercial networks
   4.3 – Biological networks
   4.4 – Social networks
5 – Clustering analysis

NAO

Fundamental:

– Elementos de apoio às aulas.

– Barabási A.L. (2016). Network science. Cambridge University Press, Cambridge.

– Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.

– Hagberg, A., Schult, D., & Swart, P. (2018). NetworkX reference, release 2.2rc1.dev20180818003440.

 – Needham, M., & Hodler, A. E. (2019). Graph algorithms: practical examples in Apache Spark and Neo4j. O’Reilly Media.

Complementary:

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

– Junker, B. H., & Schreiber, F. (2011). Analysis of biological networks (Vol. 2). John Wiley & Sons, Inc., New Jersey.

– Matthias, M. (2023). Social Media Influencers – A Review of Operations Management Literature, Master Degree thesis, University of Waterloo.

– Seyfosadat, S. F., & Ravanmehr, R. (2023). Systematic literature review on identifying influencers in social networks. Artificial Intelligence Review, 56(Suppl 1), 567-660.