CS 790g Seminar: Complex Networks

Course Information

Class hours	Monday & Wednesday, 2:30 - 3:45pm, PE 208
Instructor	Dr. Mehmet Gunes
E-mail	mgunes (at) cse (dot) unr (dot) edu
Phone	(775) 784 - 4313
Web page	http://www.cse.unr.edu/~mgunes
Office	SEM 230 (Scrugham Engineering-Mines)
Office hours	Monday & Wednesday 4:00 - 5:30 pm or by appointment
Recommended Textbooks	Dynamical Processes on Complex Networks, by Alain Barrat, Marc Barthélemy, and Alessandro Vespignani, (Cambridge University Press - November 24, 2008). Exploratory Social Network Analysis with Pajek, by Nooy, Wouter de, Andrej Mrvar, and Vladimir Batagelj, (Cambridge University Press - January 10, 2005).

Description

This course covers theory and modeling of real-world networks such as computer, social, and biological networks where the underlying topology is a dynamically growing complex graph.

Many phenomena in nature can be modeled as a network. Researchers from many areas including biology, computer science, engineering, epidemiology, mathematics, physics, and sociology have been studying complex networks of their field.

Scale-free networks and small-world networks are well known examples of complex networks where power-law degree distribution and high clustering are their respective characteristic feature. These networks have been identified in many fundamentally different systems. Complex networks display non-trivial topological features that require an in depth study.

Objective

• a broad conceptual introduction to the modern theory and applications of complex networks,
• experience critiquing scientific papers,
• experience working with large, complex data sets,
• experience with technical writing and in class presentations.

Topics (Tentative)

• Networks and Graphs
• Networks and Complexity
• Network Models
• Network Structure
• Network Dynamics
• Resilience and Robustness of Networks
• Walking and Searching on Networks
• Network Epidemics
• Traffic in Complex Networks
• Information Networks
• Infrastructure Networks
• Economic Networks
• Social Networks
• Biological Networks

Prerequisites

• Graduate level (any discipline)
• An adequate background in Calculus and Probability will be required.

Organization

• Except this web page, all course materials will be posted at the WebCT.
   
• The organization of the course will evolve as the semester progresses as I haven't taught this class before. I'm quite confident that it will be challenging but a fun course. This is not a lecture course, but an active learning opportunity with an intense engagement in research.
   
• Presentation slides will be available on the class web page. I will try to put them up before each class meeting but no guarantees on that.
   
• Students are encouraged to bring articles, demos, web pages, news events, etc. that are relevant to course topics to the attention of the instructor.
   
• Attendance at all class meetings is mandatory and will effect your grade. You should arrive on time and be prepared to discuss the session's topic. The underlying notion of the class is interaction, not passivity. The success of the course depends on everyone in the class engaging the material and bringing energy, enthusiasm, and intellect to class activities.
   
• Being a seminar course, you are primarily responsible for participating in the paper readings. You are expected to read indicated papers for each session and be prepared to discuss and comment on the material. You should brind a one paragraph summary of each paper indicated for the session. You will also provide your feedback about presentations (see presentation evaluation form).
   
• Each student will prepare a research project on a complex network of their choice. The expected outcome of the project is a research paper that can be published at a quality conference. Requiring major effort from you, the project will help in learning the culture and practice of scientific research. Late submission during project stages will be penalized by 10% per day, except holidays. Assignments will be accepted only through WebCT.
   
• The course will require students to prepare 30 min in class presentations two or three times throughout the semester. You will be graded by your peers using the presentation evaluation form. However, final grade will be decided by instructor. In the first presentation, each student will carry out a thorough review of the research related to his/her project. The second presentation should cover the methodology of your research project providing details of your approach/idea. You should indicate one or two (survey type) papers covering basics of your presentations. The selected papers should be representative of the project you are performing.
   
• You should also prepare reports for both of your presentations. Ideally, reports should become related work and methodology section in your final paper. Your review should address: the motivation for papers; the main results of the work studied; your critical assessment of the authors' work; whether the paper makes reasonable assumptions; the relation of the paper to related literature, including citations that may have been missed by the authors; and a critical evaluation of the results of the paper. Your writing should be clear, engaging, technically sound, and written in an appropriate style for an academic publication.
   
• There will be eight lab assignments (one optional) where you will have hands on experience on complex networks. These assignments will require you to use several tools (such as Pajek and GUESS) to analyze sample networks. Late submission will be penalized by 25% per day, except holidays.
   
• You are welcome to discuss the problems or solution strategies with your class mates but the resulting work should be your own. Copying from each other or from other sources is considered as cheating. Any form of cheating such as plagiarism or ghostwriting will incur a severe penalty, usually failure in the course. Please refer to the UNR policy on Academic Standards.
   
• If you have a disability for which you will need to request accommodations, please contact the instructor or someone at the Disability Resource Center (Thompson Student Services - 101) as soon as possible.
   

Research Project

The main component of your grade is a research project that should materialize as a paper. If your project has a significant computational component (e.g., downloading and analyzing a network dataset), then you may work with a partner after consulting with the instructor. The paper will be judged on the following criteria:

• Insight: Your paper should be more than just a recapitulation of existing work, or just raw analysis of data. You should make an effort to provide insight to the reader: for example, what is the data telling us about the networked system?
• Command of relevant course material: Your paper should connect to the main themes of the course, and your coverage of the related material should demonstrate competence with the content of the project.
• Clarity: You must clearly articulate the problem(s) or question(s) you are addressing; your methodology and approach; and your insights, solutions, and remaining open questions.
• Rigor and precision: Your paper must be mathematically precise where necessary, and rigorous and logical in its reasoning throughout. Any methodology used should be justified, and limitations or assumptions should be clarified.

Following are sample project topics:

• Dataset analysis: Obtain and analyze a network dataset (e.g., by downloading a dataset online, or by crawling an online service). You may perform conceptually or theoretically new experiments with existing datasets.
• Theory development: Propose a new theoretical direction and specify a research agenda. You might develop a new method to analyze networks (e.g., dynamic characteristics of biological networks).
• Network formation: Choose a specific applied domain, and discuss how networks form in that domain. For example, you might discuss the formation and dissolution of contracts among Internet service providers; the formation of links in social networks; or the evolution and dissolution of political alliances.
• Characterization of epidemics: Study several specific examples of epidemic phenomena, such as: fads in online content; virus and worm spreading in information networks; and word-of-mouth in product marketing.

Grading (Tentative)

Grading Policy

41 - Project
21 - Presentations (3)
20 - Paper reading
15 - Labs (6 of 7)
3 - Participation

Grading Scale

A : 86 - 100
B : 71 - 85
C : 56 - 70
D : 41 - 55
F : 0 - 40 (or caught cheating)

Important Note: You will have one week to appeal for your grades after the graded assignments/tests are returned. So, please keep this in mind if you think that there is a problem/issue with the grading of your work.

Similar Courses

• College of the Atlantic, Theory and Applications of Complex Networks
• Cornell University, The Structure of Information Networks
• Ecole Polytechnique Federale de Lausanne, Models and Methods for Random Networks
• Georgia Institute of Technology, Algorithms for Complex Networks
• Indiana University, Networks and Complex Systems
• Kent State University, Complex Networks
• Pennsylvania State University, Graphs and Networks in Systems Biology
• Stanford University, Network Structures and Analysis
• University of California Davis, Advanced Topics in Network Theory and Network Theory and Applications
• University of California Irvine, Networks and Complexity
• University of Essex, Social Network Analysis
• University of Helsinki, Models and Algorithms for Complex Networks
• University of Massachusetts, Scaling, Power Laws, and the Small World Phenomena in Networks
• University of Michigan, Complex Systems: Network Theory and Networks: Theory and Application
• University of Pennsylvania, Networked Life
• University of Vermont, Complex Networks
• Yale University, Graphs and Networks

Tools

• Pajek: A simple network visualization tool allowing to interactively manipulate the network. (Pajek manual)
• Graphviz: A simple network visualization tool available for a variety of platforms.
• GUESS: An exploratory data analysis and visualization tool.
• JUNG: A Java Universal Network/Graph Framework.
• NetLogo: A multi-agent programmable modeling environment.
• UCINET: A social network visualization and analysis tool.
• iGraph: A software package for creating and manipulating undirected and directed graphs.
• NetworkX: A Python package for studying the structure, dynamics, and functions of complex networks.
• IVC: InfoVis Cyberinfrastructure is a collection of data analysis and visualization algorithms.
• Net: A program for the creation and statistical analysis of large networks.
• graph-tool: A python module to help with statistical analysis.

Schedule (Tentative), Notes & Assignments

Announcements

Announcements regarding the course will be posted on this web page and WebCT. Please check your WebCT e-mail daily.

• Paper reading list
   
• Aug 18 : You can find nice figures of complex networks at www.visualcomplexity.com.
   
• Aug 18 : A recent special issue of the Science magazine is on Complex Systems and Networks.
   
• Aug 18 : Center for Complex Networks and Systems Research.
   
• Aug 22 : Lab 1 is due Tuesday Sep 8, 2009 at 2:30pm.
   
• Aug 22 : Project ideas are due Thursday Sep 9, 2009 at 1:00pm.
   
• Aug 22 : Lab 2 is due Wednesday Sep 16, 2009 at 2:30pm.
   
• Aug 31 : Notice the changes in the grading scale.
   
• Aug 31 : Paper reading summaries should be uploaded through WebCt. Note that, same of the papers are optional and submitting their summeries is a bonus to your reading grade.
   
• Sep 3 : I have posted link to a schedule for bi-weekly meetings under WebCT.
   
• Sep 11 : You need to send a draft of your presentation one week before your presentation.
   
• Sep 14 : Project report 1 is due Monday Oct 5, 2009 at 2:30pm.
   
• Sep 23 : Lab 3 is due Wednesday Sep 30, 2009 at 2:30pm.
   
• Oct 1 : For paper reviews use the Paper Review Form.
   
• Oct 7 : A good presentation on research process.
   
• Oct 8 : Lab 4 is due Monday Oct 19, 2009 at 2:30pm.
   
• Oct 8 : Project report 2 is due Monday Oct 26, 2009 at 2:30pm.
   
• Oct 8 : I will be out of town on Wednesday Oct 14, 2009 but the class will meet as scheduled.
   
• Nov 1 : Final project report is due Monday Dec 7, 2009 at 2:30pm.