Academic year 2017/2018
- Course ID
- Teaching staff
- Prof. Giancarlo Francesco Ruffo
- 2nd year
- Teaching period
- Second semester
- D.M. 270 TAF C - Related or integrative
- Course disciplinary sector (SSD)
- INF/01 - informatica
- Formal authority
- Type of examination
- A strong working knowledge of probability and linear algebra (at the
level of a bachelor degree in a scientific discipline) will certainly be helpful, as is some mathematical maturity. The ability to write code is important, because programming skills are required for the coursework project.
Sommario del corso
This module introduces the fundamental concepts, principles and methods in the interdisciplinary field of network science, with a particular focus on analysis techniques, modeling, and applications for the World Wide Web and online social media. Topics covered include graphic structures of networks, mathematical models of networks, common networks topologies, structure of large scale graphs, community structures, epidemic spreading, PageRank and other centrality measures, dynamic processes in networks, graphs visualization.
Results of learning outcomes
On successful completion of this module the students will be able to:
- Define and calculate basic network graphic metrics.
- Describe structural features of socio-technical networks.
- Relate graphic properties to network functions and evolution.
- Relate local properties to global emerging patterns.
- Explore new angles to understand network collective behaviours.
- Design and conduct analysis on large network datasets.
- Visualize networks to highlight structural and global features.
- Use network analysis tools, such as networkX library (Python), and GePhi.
A Moodle webpage is created for the course. All course materials, such as lecture notes and online resources will be shared. By using the Moodle, students will also be able to discuss ideas and questions with the lecturer and other students.
Learning assessment methods
Oral examination (60%).
Coursework I (20%): essay writing (2000-3000 words).
Coursework II (20%): individual project on network data analysis (programming is usually required).
To pass the module students must achieve a pass mark of 60% when all elements are combined.
• Introduction to complex networks
• Graph Theory and network metrics
• Random networks
• Small-world networks
• Scale-free networks
• Evolving networks
• Degree correlations
• Spreading phenomena
• Learning and games on networks
Case studies and applications
• Internet core structure - evolution and modelling
• Structure of the Web - PageRank and document networks
• Online social media networks - Twitter, Facebook, Amazon, …
• Network visualizations
• Similarity networks and recommendation systems
• "Rich gets richer" phenomenon
• Link, neighbourhood and community
• Cascades and epidemics
• Network structure balance
• Sentimental, temporal and spatial analysis of social media networks
Suggested readings and bibliography
Complex Network Analysis in Python, Recognize → Construct → Visualize → Analyze → Interpret
Author: Dmitry ZinovievEdition: P1.0
Publisher: The Pragmatic Bookshelf
Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press
Author: David Easley and Jon Kleinberg
Publisher: Cambridge University Press
Networks: an introduction
Author: Newman, Mark E. J.
Publisher: Oxford University Press