CS 765 Complex Networks
Department of Computer Science & Engineering
UNR, Fall 2018
Course Information -
| Class hours
|| Monday & Wednesday, 2:30 - 3:45pm
| Class location
|| PE 102
|| Dr. Mehmet H Gunes
|| mgunes <at>unr.edu
|| (775) 682-8313
| Web page
|| SEM 216 (Scrugham Engineering-Mines)
| Office hours
|| Monday & Wednesday 12:30 - 2:00 pm or by appointment
You may look at earlier courses from
Fall 2016, and
- Graduate level (any discipline)
- An adequate background in Calculus and Probability will be required.
Students who successfully complete this course will gain:
- a broad conceptual introduction to the modern theory and applications of network science,
- experience with mining of large data sets,
- experience critiquing scientific papers, and
- experience with technical writing and in class presentations.
Catalog Description: Theory and modeling: biological, information, social and technological networks. Network models: scale-free, small-world, power-law. Processes on networks: epidemics, resilience, search.
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 and studied using network science. 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.
- Empirical Study of Networks
- Technological networks
- Information networks
- Social networks
- Biological networks
- Economic networks
- Infrastructure networks
- Fundamentals of Network Theory
- Mathematics of networks
- Graph theory
- Measures and metrics
- Centrality and ranking
- Community structure
- Large-scale structure of networks
- Network Models
- Random graphs
- Random graphs with general degree distributions
- Power-law and scale-free graphs
- Small worlds
- Network formation
- Evolving networks
- Processes on Networks
- Percolation and network resilience
- Epidemics on networks
- Network dynamics
- Network search
- Network robustness
- Graph data mining
- Network Visualization
by Mark Newman,
(Oxford University Press, 2nd-edition - Sep 2018).
- Network Science,
by Albert-Laszlo Barabasi,
(Cambridge University Press - August 2016) -- freely available under the Creative Commons licence.
- Complex and Adaptive Dynamical Systems,
by Claudius Gros,
(Springer, 4th Edition - 2015).
- Networks, Crowds, and Markets: Reasoning About a Highly Connected World,
by David Easley and Jon Kleinberg,
(Cambridge University Press - Sep 2010) -- Pre-publication draft available online.
- The Structure of Complex Networks Theory and Applications,
by Ernesto Estrada,
(Oxford University Press - Dec 2011).
- Exploratory Social Network Analysis with Pajek,
by Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj,
(Cambridge University Press, 3rd Edition - July 2018)
- 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.
- Gephi: An open visualization and exploration software.
- GUESS: An exploratory data analysis and visualization tool.
- JUNG: A Java Universal Network/Graph Framework.
- SNAP: Stanford Network Analysis Platform.
- 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.
- graph-tool: A python module to help with statistical analysis.
- Except this web page, course materials will be posted at the WebCampus.
- This is not a lecture course, but an active learning opportunity with an intense engagement in research.
I'm quite confident that it will be challenging but a fun course.
- 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 relevant 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 using network science methodologies.
The expected outcome of the project is a research paper that can be published at a quality conference in network science.
Requiring major effort from you, the project will help in learning the culture and practice of scientific research.
Your writing should be clear, engaging, technically sound, and written in an appropriate style for an academic publication.
Late submission during project stages will be penalized by 10% per day, except holidays.
Assignments will be accepted only through WebCampus.
- You would be required to prepare reports for each stage of the project.
The first project report will be an abstract submission of the project idea related to network science.
The second report will require each student to carry out a thorough review of the research related to his/her complex network project and become the background and related work sections of the final paper.
The third report will cover the methodology of research project providing details of the network science approach/idea.
Final report will include complete paper with Introduction, Related Work, Methodology, and Evaluations sections.
- The course will require students to prepare two 30 min and one 15 min in class presentations.
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 present the background of his/her project.
The second presentation will cover the methodology of the research project.
The final presentation should present your findings and will be delivered on the final exam day.
There are several online resources, such as Research talk 101,
Presenting Your Research: Papers, Presentations, and People,
What Makes for a Good Research Presentation?,
Using PowerPoint to Design Effective Presentations, and
PowerPoint as a Powerful Tool.
- 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 seven in-class or take home quizzes. The lowest graded one will not affect your overall grade.
Exact date for these quizzes will not be exposed beforehand.
The in-class quizzes will be open book/notes and time-constrained, i.e., 10-15 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 five lab assignments 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 analyse 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.
- Academic Success Services:
Your student fees cover usage of the University Math Center (775) 784-4433, University Tutoring Center (775) 784-6801, and University University Writing Center (775) 784-6030. 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.
- Academic Dishonesty:
The University Academic Standards Policy defines academic dishonesty, and mandates specific sanctions for violations. See the University Academic Standards policy: UAM 6,502.
- Disability Statement:
Any student with a disability needing academic adjustments or accommodations is requested to speak with the Disability Resource Center (Pennington Student Achievement Center, Suite 230) as soon as possible to arrange for appropriate accommodations.
- Audio and Video Recording:
Surreptitious or covert video-taping of class or unauthorized audio recording of class is prohibited by law and by Board of Regents policy. This class may be videotaped or audio recorded only with the written permission of the instructor. In order to accommodate students with disabilities, some students may be given permission to record class lectures and discussions. Therefore, students should understand that their comments during class may be recorded.
- Equal Opportunity and Title IX:
The University of Nevada, Reno is committed to providing a safe learning and work environment for all. If you believe you have experienced discrimination, sexual harassment, sexual assault, domestic/dating violence, or stalking, whether on or off campus, or need information related to immigration concerns, please contact the University's Equal Opportunity & Title IX office at 775-784-1547. Resources and interim measures are available to assist you. For more information, please visit: https://www.unr.edu/equal-opportunity-title-ix .
The main component of your grade is a research project in network science 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. Note that such projects require time to collect data.
- 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 word-of-mouth in product marketing.
Both grading policy and scale are subject to change.
44 - Project (Abstract:3, Related Work:10, Methodology:10, Final paper:21)
12 - Presentations (5+5+2)
6 - Paper critique
20 - Labs (5)
18 - Quizzes (6 of 7)
A : [92 - 100]
A- : [88 - 92)
B+ : [84 - 88)
B : [80 - 84)
B- : [76 - 80)
C+ : [72 - 76)
C : [68 - 72)
C- : [64 - 68)
D+ : [60 - 64)
D : [56 - 60)
D- : [52 - 56)
F : [0 - 52) 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.
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 27
|| Lecture #1: Introduction
|| Connected: The Power of Six Degrees
| Wed, Aug 29
|| Lecture #2: Empirical Study of Networks
| Mon, Sep 3
|| Labor day (no class)
| Wed, Sep 5
|| Lecture #3: Mathematics of Networks
| Mon, Sep 10
|| Lecture #4: Network Metrics (components, path length, degree)
|| Project Abstract due
| Wed, Sep 12
|| Lecture #5: Network Metrics (clustering, similarity, assortativity)
| Mon, Sep 17
|| Lecture #6: Centrality (degree, betweenness, closeness, eigenvector)
|| Lab 1 due
| Wed, Sep 19
|| Lecture #7: Centrality (katz/bonacich, pagerank, hubs-authorities, lexrank)
| Mon, Sep 24
|| Lecture #8: Communities (node, group)
| Wed, Sep 26
|| Lecture #9: Communities (network, hierarchical)
|| Lab 2 due
| Mon, Oct 1
|| Lecture #10: Network Models (regular, random)
| Wed, Oct 3
|| Lecture #11: Network Models (small world)
| Mon, Oct 8
|| Lecture #12: Network Models (scale free)
|| Related Work report due
| Wed, Oct 10
|| Lecture #13: Network Evolution
| Mon, Oct 15
|| Lecture #14: Related work presentations
| Wed, Oct 17
|| Lecture #15: Related work presentations
| Mon, Oct 22
|| Lecture #16: Related work presentations - Data mining essentials
|| Lab 3 due
| Wed, Oct 24
|| Lecture #17: Related work presentations
| Mon, Oct 29
|| Lecture #18: Data mining essentials
| Wed, Oct 31
|| Lecture #19: Data mining essentials
| Mon, Nov 5
|| Lecture #20: Data mining essentials - Network Dynamics
|| Lab 4 due
| Wed, Nov 7
|| Lecture #21: Network Resilience
| Mon, Nov 12
|| Veteran's day (no class)
|| Methodology report due
| Wed, Nov 14
|| Lecture #22: Methodology presentations
| Mon, Nov 19
|| Lecture #23: Methodology presentations
|| Paper critique due
| Wed, Nov 21
|| Lecture #24: Methodology presentations - Search in networks
|| Paper critique due
| Mon, Nov 26
|| Lecture #25: Methodology presentations - Information diffusion (herd behaviour)
|| Paper critique due
| Wed, Nov 28
|| Lecture #26: Methodology presentations - Information diffusion (cascades and innovation)
| Mon, Dec 3
|| Lecture #27: Methodology presentations - Information diffusion (epidemics)
| Wed, Dec 5
|| Lecture #28: Information diffusion (networks)
| Mon, Dec 10
|| Lecture #29: Graph mining
|| Lab 5 due
| Wed, Dec 19
|| Final Project Presentations @ 12:10pm
|| Final report due @ 8am
Course Information -
Last updated on Dec 3, 2018