CS 491g/691g Special Topics on Social Networks
Department of Computer Science & Engineering
UNR, Spring 2018
Course Information -
Learning Outcomes -
Similar Courses -
Data Resources -
Research Project -
||Monday & Wednesday, 1:00 - 2:15pm
| Class location
|| PE 105
|| Dr. Mehmet H. Gunes
|| mgunes (at) unr (dot) edu
|| (775) 784 - 4313
| Web page
|| SEM 216 (Scrugham Engineering-Mines)
| Office hours
|| Monday & Wednesday 11:00 - 12:30 am or by appointment
There is no requirement for strong programming or mathematical skills.
- The objective of this course is to discuss the recent advances in online social network computing, focusing on the theoretical foundation, mathematical aspects, and applications.
- The course will introduce the tools, analytics and algorithms for the study of social networks and their data.
- The course will require students to participate in paper presentations and prepare a research project on a social network topic of their choice.
You may look at earlier course from
- (1) Students will have an an ability to apply knowledge of computing, mathematics, science, and engineering.
- (5) Students will have an ability to analyze a problem, and identify, formulate and use the appropriate computing and engineering requirements for obtaining its solution.
- (11) Students will have an ability to use current techniques, skills, and tools necessary for computing and engineering practice.
This is a tentative list of topics, subject to modification and reorganization.
- Introduction to networks
- Network analysis metrics
- Properties of networks
- Big data analytics
- Spectral analysis of networks
- Network sampling methods
- Community Structures
- Social influence analysis
- Information cascades
- Link prediction and analysis
- Recommendation in social networks
- Statistical inference
- Population dynamics
- Privacy and security in social networks
- Network Science,
by Albert-Laszlo Barabasi,
(Cambridge University Press - August 2016) -- freely available under the Creative Commons licence.
- Mining of Massive Datasets,
by Jure Leskovec, Anand Rajaraman, Jeff Ullman,
(Cambridge University Press, 2nd Edition - December 2014) -- freely available under Massive Open Online Course.
- Understanding Social Networks: Theories, Concepts, and Findings,
by Charles Kadushin,
(Oxford University Press, Jan 2012)
- Exploratory Social Network Analysis with Pajek,
by Nooy, Wouter de, Andrej Mrvar, and Vladimir Batagelj,
(Cambridge University Press, 2nd Edition - September 2011).
- Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,
by Bing Liu ,
(Springer, 2nd Edition - July 2011)
- 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.
- Networks: An Introduction,
by Mark Newman,
(Oxford University Press - May 2010).
- Social Media Mining, Huan Liu @ Arizona State University
- Social Media Mining, Reza Zafarani @ Syracuse University
- Social and Information Network Analysis, Jure Leskovec @ Stanford University
- Social Networks, Bud Mishra @ New York University
- Social Network Computing, My T. Thai @ University of Florida
- Network Analysis and Modeling, Aaron Clauset @ Santa Fe Institute
- Social Networking, Mathieu Plourde @ University of Delaware
- Networks, David Easley and Eva Tardos @ Cornell University
- Social Networks: Models, Algorithms, and Applications, Sriram V. Pemmaraju and Alberto M. Segre @ University of Iowa
- Social Networking: Technology and Society, Jennifer Golbeck @ University of Maryland, College Park
- Special Topics (Mining Information and Social Networks), Yizhou Sun @ University of California, Los Angeles
- Introduction to Social Networks, Augustin Chaintreau @ Columbia University
- Social Networks Seminar, Noah E. Friedkin @ University of California, Santa Barbara
- Social Networks and Social Network Analysis, Matthew Salganik @ Princeton University
- 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, all course materials and announcements will be posted at the WebCampus. Announcements will also be posted at https://unrsocialnetworks.slack.com. Assignments will be accepted only through 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 engagement in research.
- Presentation slides will be available on the class web page. I will try to put them up before each class meeting.
- 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 success of the course depends on everyone in the class engaging the material
and bringing energy, enthusiasm, and intellect to class activities.
- Regular attendance is highly recommended. If you miss a class, you are responsible for all material covered or assigned in class.
You should arrive on time and be prepared to discuss the session's topic.
The underlying notion of the class is interaction, not passivity.
- Unless instructed otherwise, use of electronic devices including laptops are not allowed during lectures and exams.
- Extra credit will be offered to the undergraduate students who attend the departmental colloquia (an extra point for two colloquium attendance up to 3 points). You will be reminded in class about upcoming talks but you should also check the colloquia page on a regular basis (http://www.unr.edu/cse/calendar).
- Each student will prepare a research project on a social network of their choice.
The expected outcome of the project is a research paper that can be published at a quality conference or workshop.
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.
- 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.
The second report will require each student to carry out a thorough review of the research related to his/her 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 approach/idea.
Final report will include complete paper with Introduction, Related Work, Methodology, and Evaluations sections.
- The course will require students to prepare a 18 min and a 5 min in class presentation.
You will be graded by your peers using the presentation evaluation form. However, final grade may be adjusted by the instructor.
Each student will present the background of the project they have chosen and the methodology of their research.
The final presentation should cover 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.
- There will be eight lab assignments where you will have hands on experience on different social network analysis tasks.
Late submission will be penalized by 20% per day, except holidays.
- There will be ten in-class quizzes. The lowest graded one will not affect your overall grade. Exact date for some of these quizzes will not be exposed beforehand. The quizzes will be closed 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 extra questions in assignments and quizzes for CS 691g students.
- Assignments and quizzes must be prepared strictly individually.
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 Dishonesty:
"Cheating, plagiarism or otherwise obtaining grades under false pretenses constitute academic dishonesty according to the code of this university. Academic dishonesty will not be tolerated and penalties can include filing a final grade of "F"; reducing the student's final course grade one or two full grade points; awarding a failing mark on the coursework in question; or requiring the student to retake or resubmit the coursework. For more details, see the University of Nevada, Reno General Catalog.
- 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.
- 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.
- Class 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 that may materialize as a publication.
If your project has a significant coding or computational component (e.g., downloading and analyzing a 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 analyzed characteristic in the social network?
- 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.
Both grading policy and scale are subject to change.
31 - Project (Abstract: 2, Background: 8, Methodology: 8, Final paper: 13)
10 - Presentations (Idea: 7, Results: 3)
32 - Labs (8)
27 - Quizzes (9 of 10)
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 exam dates. It is subject to readjustment depending on the time we actually spend in class covering the topics.
|| Assignments & Notes
| Mon, Jan 22
|| Lecture #1: Introduction to Social Networks
|| The hidden influence of social networks
| Wed, Jan 24
|| Lecture #2: Introduction to Data Mining
|| Connected: The Power of Six Degrees
| Mon, Jan 29
|| Lecture #3: Data Preprocessing
|| The Data Science Revolution
| Wed, Jan 31
|| Lecture #4: Networks
| Mon, Fab 5
|| Lecture #5: Graph Essentials
|| Project Abstract due
| Wed, Feb 7
|| Lecture #6: Graph Essentials
|| Lab 1 due
| Mon, Feb 12
|| Lecture #7: Network Measures
| Wed, Feb 14
|| Lecture #8: Network Measures
|| Lab 2 due
| Mon, Feb 19
|| Presidents Day (no class)
| Wed, Feb 21
|| Lecture #9: Network Models
| Mon, Feb 26
|| Lecture #10: Network Models
| Wed, Feb 28
|| Lecture #11: Student presentations
|| Related Work report due
| Mon, Mar 5
|| Lecture #12: Student presentations
| Wed, Mar 7
|| Lecture #13: Student presentations
|| Lab 3 due
| Mon, Mar 12
|| Lecture #14: Student presentations
| Wed, Mar 14
|| Lecture #15: Student presentations
|| Lab 4 due
| Mon, Mar 19
|| Spring break (no class)
| Wed, Mar 21
|| Spring break (no class)
| Mon, Mar 26
|| Lecture #16: Data Mining Essentials
| Wed, Mar 28
|| Lecture #17: Data Mining Essentials
|| Methodology report due
| Mon, Apr 2
|| Lecture #18: Data Mining Essentials
| Wed, Apr 4
|| Lecture #19: Network Centrality
|| Lab 5 due
| Mon, Apr 9
|| Lecture #20: Community Analysis
| Wed, Apr 11
|| Lecture #21: Community Analysis
|| Lab 6 due
| Mon, Apr 16
|| Lecture #22: Community Analysis
| Wed, Apr 18
|| Lecture #23: Information Diffusion
|| Lab 7 due
| Mon, Apr 23
|| Lecture #24: Information Diffusion
| Wed, Apr 25
|| Lecture #25: Influence and Homophily
|| Lab 8 due
| Mon, Apr 30
|| Lecture #26: Influence and Homophily
| Wed, May 2
|| Lecture #27: Recommendation
| Mon, May 7
|| Lecture #28: Behavior Analytics
| Mon, May 14
|| Final Project Presentations @ 9:50am
|| Final project report due
Course Information -
Learning Outcomes -
Similar Courses -
Data Resources -
Research Project -
Last updated on Feb 19, 2018