Vai al contenuto principale
Oggetto:
Oggetto:

Complex networks

Oggetto:

Complex networks

Oggetto:

Academic year 2018/2019

Course ID
MAT0049
Teaching staff
Prof. Giancarlo Francesco Ruffo
Alessandro Flammini
Year
2nd year
Teaching period
Second semester
Type
D.M. 270 TAF C - Related or integrative
Credits/Recognition
6
Course disciplinary sector (SSD)
INF/01 - informatica
Delivery
Formal authority
Language
English
Attendance
Optional
Type of examination
Mixed
Prerequisites
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.
Oggetto:

Sommario del corso

Oggetto:

Course objectives

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.

Oggetto:

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.
Oggetto:

Course delivery

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.

Oggetto:

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.

Oggetto:

Program

**Complex Networks**

• 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

• Epidemics

• Cascading Behavior in Networks

 

**Network Analysis**

 

• 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

Suggested readings and bibliography

Oggetto:

 

Complex Network Analysis in Python, Recognize → Construct → Visualize → Analyze → Interpret
Author: Dmitry ZinovievEdition: P1.0
Publisher: The Pragmatic Bookshelf
ISBN: 978-1-68050-269-5
Url: https://pragprog.com/book/dzcnapy/complex-network-analysis-in-python

Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press
Author: David Easley and Jon Kleinberg
Publisher: Cambridge University Press
ISBN: 9780521195331
Url: https://www.cs.cornell.edu/home/kleinber/networks-book/

 

**Additional Readings:**

  

Network Science
Author: Albert-László Barabási
Publisher: Cambridge University Press
ISBN: 9781107076266
Url: http://networksciencebook.com

Networks: an introduction
Author: Newman, Mark E. J.
Publisher: Oxford University Press
ISBN: 9780199206650



Oggetto:

Class schedule

Oggetto:

Note

This course is borrowed from Reti Complesse, delivered at the Computer Science Department.

Oggetto:
Last update: 19/03/2019 10:52
Location: https://www.master-sds.unito.it/robots.html
Non cliccare qui!