Course Details
Name:
Web Algorithms / Web Algorithms
Basic information
Credits:
Master Degree in Computer Science: 6 Ects (b)
Degree(s):
Compulsory 1^{st} year Master Degree in Computer Science curriculum NEDAS
Elective 1^{st} year Master Degree in Computer Science curriculum SEAS
Compulsory 1^{st} year Master Degree in Computer Science curriculum UBIDIS
Language:
English
Course Objectives
Knowledge of advanced algorithmic techniques; ability to individuate, formalize and solve optimization problems; concept of approximation; knowledge of the web search and sponsored web search strategies in search engines; ability to collaborate for the realization of applicative projects in group.
Course Content
 Review of computational complexity and intractability. Optimization problems. Approximation algorithms.
 Algorithmic techniques: greedy. local search, dynamic programming and linear programming.
 Polynomial Time Approximation Schemes (PTAS) and Fully Polynomial Time Approximation Schemes (FPTAS).
 Prestige and centrality indices in social networks.
 Web search: Pagerank, Topical Pagerank, TrustRank, Hubs and Authorities.
 Sponsored web search: matching markets and market clearing prices, auctions, VCG and GSP mechanisms.
Learning Outcomes (Dublin Descriptors)
On successful completion of this course, the student should

Acquire knowledge of advanced algorithmic techniques for NPHard optimization problems. In particular, the student will have mastery command of main algorithmic (approximation) techniques like greedy, local search, dynamic programming, linear programming: Polynomial Time Approximation Schemes (PTAS) and Fully Polynomial Time Approximation Schemes (FPTAS). Moreover the student will acquire knowledge on
the basic centrality and prestige indices in social networks, on the main popularity indices for ranking pages in web search and finally of matching markets, auctions and the most important mechanisms adopted for the ranking and payment of sponsored search links.
 Acquire the ability of abstracting models and formal algorithmic problems from real computational problems, understanding the degree of approximability and designing efficient algorithmic solutions.
 Acquire autonomy in individuating, formalizing and understanding the degree of approximability of real computational problems and identify independently their most efficient solutions.
 Being able to understand complex algorithmic solutions and to formal proving performances of their algorithmic solutions for complex computational problems.
 Acquire the ability of understanding the ranking strategies adopted by search engines in web search and sponsored web search.
 The course aims to develop in graduate students competencies and abilities necessary in their future studies and/or works, especially with respect to doctoral studies and in general to any research activity on algorithmic and web search topics.
Prerequisites and Learning Activities
KNOWLEDGE: fundamentals of programming, discrete mathematics, algorithms and data structures, computer architectures, reading and understanding of the English language
SKILLS: ability to integrate classroom and homework study, ability to interact with the teacher during the class for originating discussion.
Teaching Methods
Language: English
Lectures and exercises
Assessment Methods and Criteria
Written test followed by an oral exam. The written exam consists into two different parts (one part is related to approximation algorithm, the other one is related to websearch and sponsored websearch), and each part should be completed in 1:45 minutes. A student can decide to do the two parts in different exam dates. The minimum score to pass each part is 18/30, and the final score is the average of the two scores. An eventual oral exam consists into a detailed oral discussion of the written exam, and, eventually some further questions about the theoretical part of the course.
Textbooks
 Vijay V. Vazirani, Approximation Algorithms. Springer. 2001. ISBN: 3540653678
 G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. MarchettiSpaccamela, M. Protasi, Complexity and Approximation. Springer. 1999. ISBN: 3540654313
 Jure Leskovec, Anand Rajaraman and Jeff Ullman, Mining of Massive Datasets. Stanford University. 2011. http://infolab.stanford.edu/~ullman/mmds/book.pdf ISBN: 9781107015357
 Soumen Chakrabarti, Mining the Web – Discovering Knowledge from Hypertext Data. Morgan Kaufmann. 2003. ISBN: 9781558607545
 David Easley and Jon Kleinberg, Networks, Crowds, and Markets. Cambridge University Press. 2010. https://www.cs.cornell.edu/home/kleinber/networksbook/ ISBN: 9780521195331
Online Teaching Resources
Recent teaching material
This list contains only the latest published resources. Resources marked with an asterisk belong to other courses (indicated between brackets)
Course page updates
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Course information last updated on: 16 ottobre 2018, 16:01