Teaching Methodologies
Students are requested to follow the classes onsite or online. Lecturing involves the exposition of concepts,techniques and methods, with a strong focus on practical applications. Adequate software will be used in each topic ofthe program.
The student will be assessed by a work involving the various methodologies addressed and focusing on one of theapplied themes proposed. The final classification will result from the grade of a report of the work and from an oralpresentation of that work, in a proportion of 75% and 25% respectively.
Learning Results
A large part of the data nowadays generated have interaction relations between their elements, formingnetworks/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 havelong been studied in the scope of graph theory.
In this discipline, several of these properties are addressed, focusing on optimization techniques in networks andnetwork analysis techniques. These techniques will be used for the study of road networks, trade flow networks, socialnetworks and biological networks.
It is intended that the student knows the main characteristics and topological properties of a network/graph. It is alsointended that the student knows techniques for the analysis of networks, involving paths, flows, centrality, communityand grouping, with main motivation on social networks analysis.
Program
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
3 – Data structures for networks
2 – Tools for building and analysing networks
2.1 – Gephi
2.2 –NetworkX library from Python
3 – Study of applications resorting to network analysis
3.1 – Road networks
3.2 – Commercial networks
3.3 – Biological networks
3.4 – Social networks
4 – Clustering analysis
Internship(s)
NAO
Bibliography
– Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network flows: Theory, Algorithms and Applications. Prentice-Hall,Inc, New Jersey.
– 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.
– Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Sage, London.
– Hagberg, A., Schult, D., & Swart, P. (2018). NetworkX reference, release 2.2rc1.dev20180818003440.
– Junker, B. H., & Schreiber, F. (2011). Analysis of biological networks (Vol. 2). John Wiley & Sons, Inc., New Jersey.