Academic year 2020/2021
- Course ID
- 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.
• Introduction to complex networks
• Graph Theory and network metrics
• Strong and Weak Ties
• Structural Holes, Betweenness and Graph Partitioning
• Networks and Homophily
• A Spatial Model of Segregation
• Positive and Negative Relationships
• The Structure of the Web
• Link Analysis, PageRank, and HITS
• Spectral Analysis, Random Walks and Web Search
• Power Laws and Rich-Get-Richer Phenomena
• Long Tail and Analysis of Rich-Get-Richer Processes
• Game Theory
• Small World and Search
• Transportation Networks and Optimization
• Metabolic and River Networks
• Information Cascades
• Network Effects
• Cascading Behavior in Networks
• Elementary Networks and Tools (Python, NetworkX e Gephi)
• Networks Based on Explicit Relationships (e.g., social networks)
• Networks Measures and Centralities
• Structural Analysis
• Analysis of Networks Based on Co-Occurences
• Analysis of Similarity Networks and Recommendation Systems
• Analysis of Directed Networks
• Anaysis of Bipartite Networks
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.
Students should have be previously authorized before accessing to moodle webpages. If you need assistance, please contact the instructor.
Learning assessment methods
Disclaimer Covid-19 emergency: exam modalities declared below have been reviewed after the situation created by the current health emergency. Exams will be taken exclusively on-line, with Webex (or analogous video-conferencing system) and the link will be communicated after the enrollment to the exam is closed. The individual exams will be scheduled in function of the number of students enrolled and the individual constraints.
Progetto (30%): individual project on complex network analysis (programming in R or Python is required).
Oral examination (70%): project discussion and questions/exercises on theory.
Suggested readings and bibliography
A First Course in Network Science
Authors: Filippo Menczer, Santo Fortunato, Clayton A. Davis
Publisher: Cambridge University Press
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
Complex Network Analysis in Python, Recognize → Construct → Visualize → Analyze → Interpret
Author: Dmitry Zinoviev
Publisher: The Pragmatic Bookshelf
- Enrollment opening date
- 01/09/2019 at 00:00
- Enrollment closing date
- 30/06/2020 at 00:00