Elements of Network Science: CptS 580-06/EE 582-03 -- Syllabus

Links:   Schedule and Lecture Material   Resources

Course information

Credit hours: 3
Semester: Spring 2015
Meeting times and location: Tu-Th 12:00–13:15, Sloan 161
Course website: www.eecs.wsu.edu/~assefaw/CptS580-06

Instructor information

Assefaw Gebremedhin
Office: EME 59
Email: assefaw AT eecs DOT wsu DOT edu
Homepage: www.eecs.wsu.edu/~assefaw

Office hours

Tentative office hours: Tuesdays 3:00-4:00pm, or by appointment.

Course Description

Networks are everywhere around us (the social, economic, ecological, organizational, and political systems we operate in) and in us (in our cells and brains). This 3 credit, graduate-level course seeks to introduce fundamental elements of the emerging science of complex networks, with emphasis on social and information networks.

Objectives

Tentative list of topics to be covered include (not necessarily in this order):

• Basic network properties
• Random graphs
• The small-world phenomenon
• Power Laws and Preferential attachment
• Link analysis and Web search
• Spectral analysis
• Similarity
• Network alignment
• Community Structures
• Cascading behavior in networks
• Influence maximization
• Epidemic models

Learning outcomes

• Understand basic metrics and measures used to characterize networks
• Analyze a network using the various measures and a suitable network analysis software tool
• Understand the strengths and weaknesses of random graph models
• Understand and apply key algorithms used in web search, link analysis, and network alignment
• Understand and apply models and theories used to reason about cascading behavior and epidemics in networks
• Appreciate the interdisciplinary nature of the area of network science
• Develop better ability to critically interact with the research literature in the area

Audience

The course is suitable for graduate students in computer science, electrical engineering, applied mathematics, economics, computational social sciences, and related analytic fields.

Prerequisites

There are no formal prerequisites, but students are expected to have basic knowledge of algorithms (equivalent to completing an undergraduate algorithms course), reasonable programming experience (equivalent to completing a data structures course), and familiarity with basic linear algebra (e.g. solution of linear systems and eigenvalue/vector computation) and probability.

Course work

The course will consist of lectures (twice a week, 75 min each), and will involve a set of assignments (about five) and a semester project. A semester project could take one of several forms: analysis of an interesting dataset using existing methods and software; comparison of existing methods and software tools in the context of a specific application; implementation of a new method; exploration of a chosen research topic. Students are encouraged to work in teams of two for the semester project. Assignments, on the other hand, are to be completed and submitted individually.

Grading

• Assignments: account for 50% of the grade
• Project: accounts for 50% of the grade

Letter grades: A (93--100%), A- (90--92.99%), B+ (87--89.99%), B (83--86.99%), B- (80--82.99%), C+ (77--79.99%), C (70--76.99%), C- (67--69.99%), D (60--66.99%), F (less than 60%). Grading scale may be adjusted depending on class average.

References

There is no one official textbook for the course. Lecture notes, readings and related resources will be posted at the course website (on the "Schedule and Lecture Material" page) as the course proceeds.

The following book will be used as a frequent reference:

• D. Easley and J. Kleinberg, Networks, Crowds and Markets, Cambridge Univ. Press, 2010.
  The book is available on-line at www.cs.cornell.edu/home/kleinber/networks-book.

Other related references include:

• M.E. J. Newman, Networks: An Introduction, Oxford University Press, 2010.
• U. Brandes and T. Erlebach (Eds.), Network Analysis: Methodological Foundations, Springer, 2005.
  This book is available online via the doi: http://dx.doi.org/10.1007/b106453
• http://barabasilab.neu.edu/networksciencebook/
• Keep checking this list as I expect to add more

Policies

Missing or late work

Except by prior arrangement, missing or late work will be counted as a zero.

Academic Integrity

Academic integrity will be strongly enforced in this course. Any student who violates the University's standard of conduct relating to academic integrity will receice an F as a final grade in this course, will not have the option to withdraw from the course and will be reported to the Office of Student Standards and Accountability. Cheating is defined in the Standards for Student Conduct WAC 504-26-010 (3). You can learn more about Academic Integrity on the WSU campus at http://conduct.wsu.edu. Please also read this link carefully: EECS Academic Integrity Policy. Use these resources to ensure that you do not inadvertently violate WSU's standard of conduct.

Safety on Campus

Washington State University is committed to enhancing the safety of the students, faculty, staff, and visitors. It is highly recommended that you review the Campus Safety Plan (http://safetyplan.wsu.edu/) and visit the Office of Emergency Management web site (http://oem.wsu.edu/) for a comprehensive listing of university policies, procedures, statistics, and information related to campus safety, emergency management, and the health and welfare of the campus community.

Students with Disabilities

Reasonable accommodations are available for students with a documented disability. If you have a disability and need accommodations to fully participate in this class, please either visit or call the Access Center (Washington Building 217; 509-335-3417) to schedule an appointment with an Access Advisor. All accommodations MUST be approved through the Access Center.

Weather Policy

For emergency weather closure policy, consult: http://alert.wsu.edu.

Changes

This syllabus is subject to change. Updates will be posted on the course website.