We are surrounded by networks. The Internet, one of the most advanced artifacts ever created by mankind, is the paradigmatic example of a "network of networks" with unprecedented technological, economical and social ramifications. Online social networks have become a major driving phenomenon on the web since the Internet has expanded as to include users and their social systems in its description and operation. Technological networks such as the cellular phone network or the energy grid support many aspects of our daily life. Moreover, there is a growing number of highly-popular user-centric applications in Internet that rely on social networks for mining and filtering information, for providing recommendations, as well as for ranking of documents and services. In this course we will present the design principles and the main structural properties and theoretical models of online social networks and technological networks, algorithms for data mining in social networks, and the basic network economic issues, with an eye towards the current research issues in the area.

Topics that we will cover

• Properties of social networks
• Models for social networks
• Community detection
• Spectral techniques for community detection
• Centrality measures
• Deep learning and network embeddings
• Cascading behavior in social networks and epidemics
• Influence maximization and viral marketing
• Influence and homophily
• Game theory on networks
• Network traffic
• Selfish routing and price of anarchy
• Auctions
• Algorithmic market design
• Market Equilibria: characterization and computation
• Two-sided markets and sharing economy

 

Announcements

Remember to register your email; details will be given in class.

We start classes on February 25.

 

Instructors

Aris Anagnostopoulos, Sapienza University of Rome

Chris Schwiegelshohn, Sapienza University of Rome

 

When and where:

Monday 14.00–16.00, Via Ariosto 25, Room A7

Wednesday 14.00–17.00, Via Ariosto 25, Room A5

 

Office hours

You can use the office hours for any question regarding the class material, general questions on networks, the meaning of life, pretty much anything. Send an email to the instructors for arrangement.

 

Textbook and references

The main textbook is the book Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by David Easley and Jon Kleinberg.

Another book we will use is Algorithms and Models for Network Data and Link Analysis, by François Fouss, Marco Saerens, and Masashi Shimbo.

In addition, we will cover material from various other sources, which we will post online as the course proceeds.

 

Exam format

The evaluation has two parts: a set of three theoretical homeworks, which will be due during the semester, and a final project. During the exam period we will assess the quality of the projects and the extent you have learned the class material. Students are highly encouraged to do the homeworks. Whoever does not do the homeworks will have to do a final written exam on the entire class material and a final project. In this case as well we will have an oral exam during the exam session.

 

Syllabus

Date	Topic	Reading
February 25	Introduction to social networks and online markets.	Chapters 1, 2
February 27	Properties of social and complex networks, tie strength.	Chapter 3, slides
March 4	Tie strength (cont.), affiliation networks	Chapter 4
March 6	Random-graph models for social networks	Notes
March 13	Navigability, preferential attachment	Notes
March 18	Basics of linear algebra for social networks
March 20	Graph Laplacian and Cheeger’s inequality (lower bound)
March 25	Introduction to game theory	Chapter 6
March 27	Basic auction theory	Chapter 9
April 01	Cheeger’s inequality (upper bound)
April 03	Exact recovery of communities
April 08	Densest subgraph
April 10	Sparsification
April 15	Max-cut approximation
April 17	Sparsest cut and metric embeddings
May 13	Sponsored search	Chapter 10
May 15	Sponsored search (cont.), Indroduction to neural networks, word2vec	Tutorial on word2vec, paper 1, paper 2
May 20	Introduction to neural-network-based graph embeddings	Tutorial on graph embeddings, DeepWalk, node2vec

 

Homeworks

• Homework 1 (due: 25/6/2019, 23.59)

 

Collaboration policy (read carefully!): You can discuss with other students of the course about the homeworks and the projects. However, you must understand well your solutions and the final writeup must be yours and written in isolation. In addition, even though you may discuss about how you could implement an algorithm, what type of libraries to use, and so on, the final code must be yours. You may also consult the internet for information, as long as it does not reveal the solution. If a question asks you to design and implement an algorithm for a problem, it's fine if you find information about how to resolve a problem with character encoding, for example, but it is not fine if you search for the code or the algorithm for the problem you are being asked. For the projects, you can talk with other students of the course about questions on the programming language, libraries, some API issue, and so on, but both the solutions and the programming must be yours. If we find out that you have violated the policy and you have copied in any way you will automatically fail. If you have any doubts about whether something is allowed or not, ask the instructor.