CS 790g Seminar: Complex Networks
Department of Computer Science & Engineering
UNR, Fall 2009
Course Information -
| Class hours
|| Monday & Wednesday, 2:30 - 3:45pm, PE 208
|| Dr. Mehmet Gunes
|| mgunes (at) cse (dot) unr (dot) edu
|| (775) 784 - 4313
| Web page
|| SEM 230 (Scrugham Engineering-Mines)
| Office hours
|| Monday & Wednesday 4:00 - 5:30 pm or by appointment
| 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).
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.
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.
- 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
- Graduate level (any discipline)
- An adequate background in Calculus and Probability will be required.
- 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.
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.
Both grading policy and scale are subject to change.
41 - Project
21 - Presentations (3)
20 - Paper reading (20 of 22)
15 - Labs (6 of 7)
3 - Participation
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.
- 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,
- 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,
- University of Vermont,
- Yale University,
Graphs and Networks
- 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.
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.
|| Assignments & Notes
| Mon, Aug 24
|| Lecture #1: Introduction
|| - Paper reading list
- How to read a research paper
| Wed, Aug 26
|| Lecture #2: Network properties by Gunes.
|| - Read fist three chapters of Statistical mechanics of complex networks
and The structure and function of complex networks.
| Mon, Aug 31
|| Lecture #3: Small worlds by Gunes.
|| - Read Collective dynamics of ‘small-world’ networks
and Navigation in a small world
and The small world web
and Networks, Dynamics and the Small World Phenomenon (optional).
| Wed, Sep 2
|| Lecture #4: Internet topology discovery by Oz
and Scale-free networks by Gunes.
| - Read Diameter of the World-Wide Web
and Revisiting 'scale-free' networks
and Internet Topology Discovery
and Measuring ISP Topologies with Rocketfuel (optional).
| Mon, Sep 7
|| Labor Day (no class)
|| - Lab 1 due Tuesday Sep 8, 2009 at 2:30pm (Pajek manual)
| Wed, Sep 9
|| Lecture #5: Community structure detection by Badepalli
and Frequent subgraph discovery by Kardes.
| - Read Frequent Subgraph Discovery
and Graph-based induction
and Detecting community structure in networks.
| Mon, Sep 14
|| Lecture #6: Complex network visualization by Shelley
and Search in structured networks by Gunes.
| - Read How to search a social network
and The art and science of dynamic network visualization.
| Wed, Sep 16
|| Lecture #7: Epidemics in Blogspace by Karaoglu
and Information diffusion in networks by Gunes.
| - Read Tracking Information Epidemics in Blogspace
and The Structure of Information Pathways in a Social Communication Network.
- Lab 2 due at 2:30pm.
| Mon, Sep 21
|| Lecture #8: Software Systems by Jayaprakash
and WordNet by Velmurugan.
| - Read Software systems as complex networks
and Word Sense Determination using WordNet and Sense Co-occurrence
and Topology of the conceptual network of language (optional).
| Wed, Sep 23
|| Lecture #9: Image Classification by Li
and Mapping peer2peer networks by Mercan.
| - Read Unsupervised modeling of object categories using link analysis techniques
and A complex network-based approach for boundary shape analysis (optional)
and Mapping the Gnutella Network.
| Mon, Sep 28
|| Lecture #10: Myosin network by Jackson
and Tag networks by Kilavuz.
| - Read How Well Can We Understand Large-Scale Protein Motions Using Normal Modes of Elastic Network Models?
and Position Paper, Tagging, Taxonomy, Flickr, Article, ToRead
and Visualizing Tags over Time (optional)
| Wed, Sep 30
|| Lecture #11: Funding networks by Patel
and Influenza dynamics by Breland
| - Read The NBER Patent Citations Data File
and Networks and epidemic models
and Seasonality and the dynamics of infectious diseases (optional)
- Lab 3 due at 2:30pm.
| Mon, Oct 5
|| Lecture #12: Network visualization by Gunes
Visualizing the Internet topology by Shelley
| - Read report posted on WebCT.
- Project report 1 due at 2:30pm.
| Wed, Oct 7
|| Lecture #13: Network centrality by Gunes
Research funding networks by Patel
| Mon, Oct 12
|| Lecture #14: Graph data mining by Gunes
Graph based induction by Kardes
| Wed, Oct 14
|| Lecture #15: Internet topology mapping by Oz
| Mon, Oct 19
|| Lecture #16: Network resilience by Gunes
Understanding Myosin network by Jackson
| - Lab 4 due at 2:30pm.
| Wed, Oct 21
|| Lecture #17: Networks in the Web by Gunes
Flickr tags network by Kilavuz
| Mon, Oct 26
|| Lecture #18: Community structures by Gunes
Class Collaboration Graphs by Jayaprakash
| - Project report 2 due at 2:30pm.
| Wed, Oct 28
|| Lecture #19: Lexical networks by Gunes
Word Sense Disambiguation by Velmurugan
| Mon, Nov 2
|| Lecture #20: Networks over time by Gunes
|| - Lab 5 due at 2:30pm.
| Wed, Nov 4
|| Lecture #21: Overlay networks by Mercan
Hoax or Truth by Karaoglu
| Mon, Nov 9
|| Lecture #22: Image characterization neyworks by Li
Influenza network by Breland
| Wed, Nov 11
|| Veterans Day (no class)
| Mon, Nov 16
|| Lecture #23: Language networks by Velmurugan
Topology generators by Mercan
| - Lab 6 due at 2:30pm.
| Wed, Nov 18
|| Lecture #24: Foundations of network analysis by Shelley
Structural Similarities by Li
| Mon, Nov 23
|| Lecture #25: Internet Ecosystem by Oz
Graph indexing by Kardes
| Wed, Nov 25
|| Lecture #26: Metanetwork Analysis by Kilavuz
Predicting behavior of techno-social systems by Karaoglu
| Mon, Nov 30
|| Lecture #27: Economic networks by Jayaprakash
Collaboration networks by Patel
| - Lab 7 due at 2:30pm. (optional)
| Wed, Dec 2
|| Lecture #28: Cardiac Myosin Network by Jackson
Web of life by Breland
| Mon, Dec 7
|| Snow Day (no class)
|| Wed, Dec 9
|| Progress Reports
|Prep Day (no class) | - Final project report due at 2:30pm.
| Mon, Dec 14
at 2:15 pm
| Final Exam
(Well, we have no Finals!!!)
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.
- Nov 16 : Final project report is extended to Wednesday Dec 9, 2009 at 2:30pm.
Note that, while late policy (10% penalty per day) applies, reports will not be accepted after Dec 15th - 2:30pm.
- Nov 18 : Up to 50% bonus will be added to the project reports that are at a quality that can be published at a good conference, workshop or symposium.
Course Information -
Last updated on Dec 8, 2009