CS 765 Complex Networks
Department of Computer Science & Engineering
UNR, Fall 2014
Course Information 
Description 
Objective 
Topics 
Prerequisites 
Organization 
Grading 
Courses 
Tools 
Papers 
Schedule 
Announcements
Class hours 
Tuesday & Thursday, 9:30  10:45am 


Class location 
SEM 201 (AGN) 
Instructor 
Dr. Mehmet Gunes 
Email 
mgunes <at>unr.edu 
Phone 
(775) 784  4313 
Web page 
http://www.cse.unr.edu/~mgunes 
Office 
SEM 238 (Scrugham EngineeringMines) 
Office hours 
Tuesday & Thursday 11:00 am  12:30 pm or by appointment 
Recommended Textbooks
 Networks: An Introduction,
by Mark Newman,
(Oxford University Press  May 20, 2010).
 Networks, Crowds, and Markets: Reasoning About a Highly Connected World,
by David Easley and Jon Kleinberg,
(Cambridge University Press  Sep 2010) Full text available online.
 The Structure of Complex Networks Theory and Applications,
by Ernesto Estrada,
(Oxford University Press  Dec 17, 2011).
 Dynamical Processes on Complex Networks,
by Alain Barrat, Marc Barthelemy, and Alessandro Vespignani,
(Cambridge University Press  November 24, 2008).
 The Structure and Dynamics of Networks,
by Mark Newman, AlbertLaszlo Barabasi, & Duncan J. Watts,
(Princeton University Press  April 17, 2006).
 Exploratory Social Network Analysis with Pajek,
by Nooy, Wouter de, Andrej Mrvar, and Vladimir Batagelj,
(Cambridge University Press  January 10, 2005).
You may look at earlier courses from Fall 2009,
Fall 2010,
Fall 2011, and
Spring 2013.
Catalog Description: Theory and modeling: biological, information, social and technological networks.
Network models: scalefree, smallworld, powerlaw. Processes on networks: epidemics, resilience, search.
This course covers theory and modeling of realworld 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.
Scalefree networks and smallworld networks are well known examples of complex networks
where powerlaw degree distribution and high clustering are their respective characteristic feature.
These networks have been identified in many fundamentally different systems.
Complex networks display nontrivial topological features that require an in depth study.
Students who successfully complete this course will gain:
 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.
 Empirical Study of Networks
 Technological networks
 Information networks
 Social networks
 Biological networks
 Economic networks
 Infrastructure networks
 Fundamentals of Network Theory
 Mathematics of networks
 Measures and metrics
 Largescale structure of networks
 Network Models
 Random graphs
 Random graphs with general degree distributions
 Powerlaw
 Small worlds
 Network formation
 Processes on Networks
 Percolation and network resilience
 Epidemics on networks
 Network dynamics
 Network search
 Graph data mining
 Network Visualization
 Graduate level (any discipline)
 An adequate background in Calculus and Probability will be required.
 Except this web page, course materials will be posted at the WebCampus.
 The organization of the course will evolve as the semester progresses.
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.
 Class participation in terms of asking questions is highly encouraged.
Please do not be afraid to ask questions no matter how simple you might think the answer could be.
This type of interaction helps improve the effectiveness of the class and breaks the monotony.
 Students are encouraged to bring articles, demos, web pages, news events, etc. that are relevant to course topics to the attention of the instructor.
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.
 Unless instructed otherwise, use of electronic devices including laptops and smart phones are not allowed during lectures.
 Attendance at all class meetings is mandatory and will affect your grade.
You should arrive on time and be prepared to discuss the session's topic.
 Being a graduate course, you are primarily responsible for participating in the discussion.
You are expected to read indicated papers and chapters for each session and be prepared to discuss and comment on the material.
 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 WebCampus.
 The course will require students to prepare two 30 min and one 15 min in class presentations 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.
The final presentation should present your findings.
 You would be required to prepare reports for each of the presentations.
These reports would become related work, methodology, and evaluations sections in your final paper.
Your writing should be clear, engaging, technically sound, and written in an appropriate style for an academic publication.
 You will critique your peer's papers using paper review form.
The goal of this critique is to become familiarized with paper review process and to provide feedback to your fellows in the class.
 There will be six inclass quizzes. The lowest graded one will not affect your overall grade.
Exact date for these quizzes will not be exposed beforehand.
These quizzes will be open book/notes and extremely timeconstrained, i.e., 1520 mins.
Questions in these quizzes will be designed to give you an opportunity to test and affirm your knowledge of the course content.
 There will be six lab assignments where you will have hands on experience on complex networks.
The lowest graded one will not affect your overall grade.
These assignments will require you to use several tools (such as Pajek and GUESS) to analyze sample networks.
Late submission will be penalized by 20% 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.
 Surreptitious or covert videotaping of class or unauthorized audio recording of class is prohibited by law and by Board of Regents policy.
This class maybe videotaped or audio recorded only with the written permission of the instructor.
In order to accommodate students with disabilities, some students may have been given permission to record class lectures and discussions.
Therefore, students should understand that their comments during class may be recorded.
 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.
 Academic Success Services: Your student fees cover usage of the
Math Center (7844433 or www.unr.edu/mathcenter),
Tutoring Center (7846030 or www.unr.edu/tutoring), and
University Writing Center (7846801 or http://www.unr.edu/writing_center).
These centers support your classroom learning; it is your responsibility to take advantage of their services.
Keep in mind that seeking help outside of class is the sign of a responsible and successful student.
The main component of your grade is a research project that may 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.
 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.
 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).
 Characterization of epidemics: Study several specific examples of epidemic phenomena,
such as: fads in online content; virus and worm spreading in information networks; and wordofmouth in product marketing.
Both grading policy and scale are subject to change.
Grading Policy
46  Project (Abstract:3, Related Work:10, Methodology:10, Final paper:23)
14  Presentations (2 + 1/3)
10  Paper critique
15  Labs (5 of 6)
15  Quizzes (5 of 6)
Grading Scale
A : 87  100
B : 75  86
C : 63  74
D : 51  62
F : 0  50 (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.
 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 multiagent 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.
 graphtool: A python module to help with statistical analysis.
This is a tentative schedule including the assignment dates.
It is subject to readjustment depending on the time we actually spend in class covering the topics.
Date 
Lectures 
Assignments & Notes 
Tue, Aug 26 
Lecture #1: Introduction 

Thu, Aug 28 
Lecture #2: Empirical Study of Networks 
Project Ideas 
Tue, Sep 2 
Lecture #3: Mathematics of Networks 

Thu, Sep 4 
Lecture #4: Mathematics of Networks (cont) 

Tue, Sep 9 
Lecture #5: Centrality 
Lab 1 due  Project Title/Abstract due 
Thu, Sep 11 
Lecture #6: Centrality (cont) 

Tue, Sep 16 
Lecture #7: Centrality (cont) 
Lab 2 due 
Thu, Sep 18 
Lecture #8: Community Structures 

Tue, Sep 23 
Lecture #9: Community Structures (cont)  Measures and Metrics 

Thu, Sep 25 
Lecture #10: Network Models 
Lab 3 due 
Tue, Sep 30 
Lecture #11: Scale Free Networks 

Thu, Oct 2 
Lecture #12: Small Worlds 
Research talk 101
Presenting Your Research: Papers, Presentations, and People
What Makes for a Good Research Presentation? 
Tue, Oct 7 
Lecture #13: Small Worlds 
Using PowerPoint to Design Effective Presentations
PowerPoint as a Powerful Tool

Thu, Oct 9 
Lecture #14: Internet Topology  Internet Mapping 
Related Work report due 
Tue, Oct 14 
Lecture #15: Networks on Rails and Reddit 

Thu, Oct 16 
Lecture #16: Lyrics and Ebola 

Tue, Oct 21 
Lecture #17: Energy, Water, and Environment 

Thu, Oct 23 
Lecture #18: Bitcoin 

Tue, Oct 28 
Lecture #19: Migration  Internet Sampling 
Lab 4 due 
Thu, Oct 30 
Lecture #20: Network Dynamics 

Tue, Nov 4 
Lecture #21: Network Evolution  Internet Sampling 

Thu, Nov 6 
Lecture #22: Internet Mapping  Networks on Rails 
Methodology report 
Tue, Nov 11 
Veteran's day (no class) 

Thu, Nov 13 
Lecture #23: Energy, Water, and Environment  Ebola 

Tue, Nov 18 
Lecture #24: Bitcoin  Lyrics 
Paper critique 1 
Thu, Nov 20 
Lecture #25: Migration  Reddit 
Paper critique 2 
Tue, Nov 25 
Lecture #26: Search in Networks 

Thu, Nov 27 
Thanksgiving (no class) 

Tue, Dec 2 
Lecture #27: Information Diffusion 
Lab 5 due 
Thu, Dec 4 
Lecture #28: Network Resilience 

Tue, Dec 9 
Lecture #29: Graph Mining 
Lab 6 due 
Tue, Dec 16 
Final Project Presentations @ 12:30pm 
Final report due 
Announcements regarding the course will be posted on this web page
and sent by email to your UNR email account.
Please daily check your UNR email.
Course Information 
Description 
Objective 
Topics 
Prerequisites 
Organization 
Grading 
Courses 
Tools 
Papers 
Schedule 
Announcements
Last updated on Dec 16, 2014