An introduction to Network Science, this is a half lecture half seminar course. Networks model the relationships in complex systems, from hyperlinks between web pages, and co-authorships between research scholars to biological interactions between proteins and genes, and synaptic links between neurons. Network Science is an interdisciplinary research area involving researchers from Physics, Computer Science, Sociology, Math and Statistics, with applications in a wide range of domains including Biology, Medicine, Political Science, Marketing, Ecology, Criminology, etc. In this course, we will cover the basic concepts and techniques used in Network Science, review the state of the art techniques, and discuss the most recent developments.
This course requires programming skills (Python) and basic knowledge of linear algebra. This is a seminar based course, and a decent Internet connection is also required to be able to engage with the class.
project revised report and rebuttal due on Dec. 20th
5% reviewing assignments
25% presentations of assigned papers
30% assignments (3x10%)
40% project (10% proposal, 10% progress report, 20% final report)
note: most of the grading is by peer-assessment
Late submission policy
All due dates are 11:59 pm in Montreal.
For assignments, 2^k% of the grade will be deducted per k days of delay.
Project deadlines are firm and no extension is possible [given the peer-review assessment, it is not possible to delay the submissions, but you will have ample time to improve and resubmit, so try to deposit a version by the checkpoints regardless of the stage your report is at]. The final deposit of Dec. 20th has the same late policy as the assignments, it you need extra time at that point.
For presentations, if you are going to miss your assigned class presentation, you need to arrange for another student to switch with you. If you can not find someone to cover for you, you need to contact me so that we can find other arrangements. Other than special situations, you most likely will lose the grade.
note: we will use this sheet throughout the course for planning the presentations
Try to discuss the main idea and results. You don't need to be comprehensive and cover all the points in the paper. Only what you find interesting to share and the key contributions of the paper.
You can also safely assume that the students know what we have discussed in the class, so you don't need to go over the basics, only what this paper adds to the class discussions.
Target a timing of 12 minutes +-2 minutes. (total 15, including time for questions)
Try to spend equal time (1-2 minutes) on problem def, motivation, main intuition, methodology, experiment setup (data, tasks, evaluation), main finding, and results
Introduction and Motivation, Related Work, Problem Definition, Dataset Description
Progress report [4-5 pages]
(extended, improved proposal) + Methodology, Experiment Setup, and Preliminary results
Final report [8 pages + a reference only page]
(extended, improved progress report) + Final Results, Evaluation, Discussion, Conclusions
For examples see the reports from a similar course here
For the presentations, cover the same components in the report with roughly equal emphasis on each section, allocate some time for QA and feedback.
For proposals, this is pitching your project, what is motivating it, what is the graph you are going to look at, and how you are going to analyze it.
For examples of short presentations, see the videos for papers at virtual conferences, for example this year's KDD.
In the Scope of Graph Mining
problem is defined to gain insights through graphs, or to build a tool which helps us for that
Strong related work section
explains what is related to this project, the current state of the art, categorizes, draws connection between them and the proposed method, compares them against the proposed method (~ 15 papers)
Some degree of originality
incremental ideas and/or not beating the state of the art will not affect the grade, but some creativity should be demonstrated
easy to follow and well-thought, proper use of formulations
Well presented Results
Data explained, proper and well-thought evaluation, polished format & visuals, highlighting the main findings and conclusions
When applicable: comparing with baselines, variation of the model, sensitivity analysis, complexity analysis
We have a mixed lecture-seminar style course. I will cover the fundamental and classic concepts related early in the course and we move on more seminar series on the most recent works related to the discussed topics later in the course.
Early in the course, we will also have 3 sets of hands-on exercise problems (10% each) to implement basic algorithms and get accustomed to working with networked data. Later on, we move to the project, designed as an intro to research in network science.
Except for the related work section of your project reports, all other parts should be 100% your own deductions, implementation, findings, and results. For the related work section, you may rephrase and summarize the findings of relevant prior work, and cite the corresponding paper. It is not acceptable to copy anything or reuse any wordings, other than technical terms, with appropriate citations. Plagiarism, if detected, not only will result in zero in your grade, but also a report to the university.