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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
note: dates are tentative, please check them for the updated deadlines
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
Summary of Course Structure
Each week we discuss a topic in network science. I will cover the fundamental and classic concepts related to the topic of the week and provide a list of five papers for further reading on the most recent works related to the discussed topic. In a subsequent session, five students present those readings in 15-minute time slots, 10-minute presentations, 5-minute discussion (similar to conference presentations). When discussing these state of the art papers, we brainstorm over the open problems related to the topic, and possible directions to follow up on these recent works. This could become a topic that a student could choose to delve deeper into for his/her project (we will also have a list of topics from the start of the course to choose from). Projects are led by one student, and collaborations are encouraged, but you will be only graded based on the project you lead (bonus points are the exception and will be given to all collaborators, see grading details). 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. For the project, you will deliver a 5-minute pitch on your project topic, submit a 2 pages project proposal mid-term, 4-5 pages project progress report a month later and a final project report at the end of the term. The proposal covers the main related works, problem definition, datasets, and experiment setup. You will present the proposals in 5 minutes of presentation (2-4 slides) to get quick feedback. The project progress report is 4-5 pages and adds the preliminary results. The final report is expected as an 8 page write up and has the final results, which will be reviewed by two/three of your peers. Based on the reviews, you can submit a 500 word rebuttal and an improved final version. The final version will be graded by me and the TA taking into account all the reviews and the rebuttal. All submissions for the project are in a conference proceeding format (you are strongly encouraged to use latex). This setup closely mimics a research project lifecycle, from forming the idea to publish the results, and is 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.