CS 491g/691g Special Topics on Social Networks
Department of Computer Science & Engineering
UNR, Fall 2016
Course Information -
Similar Courses -
Research Project -
| Class hours
||Tuesday & Thursday, 1:00 - 2:15pm
| Class location
|| SEM 234
|| Dr. Mehmet H. Gunes
|| mgunes (at) unr (dot) edu
|| (775) 784 - 4313
| Web page
|| SEM 238 (Scrugham Engineering-Mines)
| Office hours
|| Tuesday & Thursday 9:30 - 11:00 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 is designed mainly for students who are interested in the social network analysis.
- The course will require students to participate in paper presentations and prepare a research project on a social network topic of their choice.
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
- Social Media Mining: An Introduction,
by Reza Zafarani, Mohammad Ali Abbasi, Huan Liu,
(Cambridge University Press) -- Pre-publication draft available online.
- Mining of Massive Datasets,
by Jure Leskovec, Anand Rajaraman, Jeff Ullman,
(Cambridge University Press, 2nd Edition - December 2014).
- Exploratory Social Network Analysis with Pajek,
by Nooy, Wouter de, Andrej Mrvar, and Vladimir Batagelj,
(Cambridge University Press, 2nd Edition - September 2011).
- Network Science,
by Albert-Laszlo Barabasi,
(Cambridge University Press - August 2016) -- freely available under the Creative Commons licence.
- Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,
by Bing Liu ,
(Springer, 2nd Edition 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).
- Community Detection and Mining in Social Media,,
by Lei Tang and Huan Liu,
(Morgan & Claypool Publishers, Jan 2010)
- Understanding Social Networks: Theories, Concepts, and Findings,,
by Charles Kadushin,
(Oxford University Press, Jan 2012)
- Provenance Data in Social Media,,
by Geoffrey Barbier, Zhuo Feng, Pritam Gundecha, and Huan Liu,
(Morgan & Claypool Publishers, May 2013)
- Mining Human Mobility in Location-Based Social Networks,,
by Huiji Gao and Huan Liu,
(Morgan & Claypool Publishers, Apr 2015)
- Modeling and Data Mining in Blogosphere,,
by Nitin Agarwal and Huan Liu,
(Morgan & Claypool Publishers, 2009)
- Social Network Analysis: Methods and Applications, by Stanley Wasserman and Katherine Faust,
(Cambridge University Press, Nov 1994)
- Social Media Mining, Reza Zafarani @ Syracuse University
- Social Media Mining, Huan Liu @ Arizona State 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 in Data (Mining Information/Social Networks), Yizhou Sun @ University of California, Los Angeles
- Social Networks, Augustin Chaintreau @ Columbia University
- Introduction to Social Networks , Augustin Chaintreau @ Columbia University
- Social Networks Seminar, Noah E. Friedkin @ University of California, Santa Barbara
- Except this web page, all 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 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.
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 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 complex 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.
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.
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 25 min (Graduate: 35 min) and a 10 min (Graduate: 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.
- There will be seven 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. These 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 extra questions in assignments and quizzes for CS 691g students.
- Assignments and exams 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 canceling a student's enrollment without a grade, giving an F for the course or for the assignment. 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.
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 measured 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.
32 - Project (Abstract: 2, Background: 8, Methodology: 8, Final paper: 14)
12 - Presentations (1 + 1/2)
21 - Labs (7)
35 - 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
|| Assignments & Notes
| Tue, Aug 30
|| Lecture #1: Introduction to Social Networks
|| The hidden influence of social networks
| Thu, Sep 1
|| Lecture #2: Introduction to Social Networks
|| Connected: The Power of Six Degrees
| Tue, Sep 6
|| Lecture #3: Introduction to Data Mining
|| The Data Science Revolution
| Thu, Sep 8
|| Lecture #4: Graph Essentials
| Tue, Sep 13
|| Lecture #5: Network Measures
|| Project Abstract due
| Thu, Sep 15
|| Lecture #6: Network Measures
| Tue, Sep 20
|| Lecture #7: Network Measures
| Thu, Sep 22
|| Lecture #8: Network Models
|| Lab 1 due
| Tue, Sep 27
|| Lecture #9: Network Models
| Thu, Sep 29
|| Lecture #10: Data Mining Essentials
|| Lab 2 due
| Tue, Oct 4
|| Lecture #11: Data Mining Essentials
| Thu, Oct 6
|| Lecture #12: Data Mining Essentials
| Tue, Oct 11
|| Lecture #13: Data Mining Essentials
| Thu, Oct 13
|| Lecture #14: Community Analysis
|| Related Work report due
| Tue, Oct 18
|| Lecture #15: Community Analysis
| Thu, Oct 20
|| Lecture #16: Community Analysis
|| Lab 3 due
| Tue, Oct 25
|| Lecture #17: Network Visualization - Graph Indexing
|| Research talk 101
| Thu, Oct 27
|| Lecture #18: Venture Recommendation - Impairment Awareness
| Tue, Nov 1
|| Lecture #19: Viral Content - Censorship - Memory Standardization
| Thu, Nov 3
|| Lecture #20: Netflix Ratings - Predictions
| Tue, Nov 8
|| Lecture #21: Political Positions - Amazon Reviews - LinkedIn
|| Methodology report due
| Thu, Nov 10
|| Lecture #22: Longer Tail - Like-Minded Users - Lindy Hop Community
|| Lab 4 due
| Tue, Nov 15
|| Lecture #23: Information Diffusion
| Thu, Nov 17
|| Lecture #24: Information Diffusion
|| Lab 5 due
| Tue, Nov 22
|| Lecture #25: Information Diffusion
| Thu, Nov 24
|| Thanksgiving (no class)
| Tue, Nov 29
|| Lecture #26: Influence and Homophily
|| Lab 6 due
| Thu, Dec 1
|| Lecture #27: Influence and Homophily
| Tue, Dec 6
|| Lecture #28: Recommendation
| Thu, Dec 8
|| Lecture #29: Behavior Analytics
| Tue, Dec 13
|| Lecture #30: Final Project Presentations (graduate)
|| Lab 7 due
| Tue, Dec 20
|| Final Project Presentations @ 5:00pm
|| Final project report due
Announcements regarding the course will be posted on this web page
and sent by e-mail to your UNR e-mail account.
Please daily check your UNR e-mail.
Course Information -
Similar Courses -
Research Project -
- Dec 8 : Office hours on Monday, Dec 12 between 12-1:30pm instead of Dec 13th.
Last updated on Dec 8, 2016