ECS637U/ECS757P - Digital Media and Social Networks Group Project
This coursework should be done in groups of 3-4 people and is assessed in two deliverables
(deadlines below). The deadline for forming groups is 10pm 9 February: After this you will be
allocated to a group. If you do not have a “full” group, you will be automatically assigned new
group members after this time. The total grade weight for this coursework is 30% (15% for
deliverable 1 and 15% for deliverable 2).
NB: You may only form groups with students at the same level (Groups may not contain a
mix of undergraduate and postgraduate students).
Amendment History
Date Description Person
Feb 20th Clarifications to description Mathieu Barthet
Feb 24th Further breakdown of marking guidelines for
Lightning Talks and minor clarifications
Laurissa Tokarchuk
Mar 11th Amended final date to match QMPlus hand
in on main page
Laurissa Tokarchuk
Mar 24th Further extension to the final hand in Laurissa Tokarchuk
Preliminary work
As a group ensure that you have each read three or four of the essential reading papers listed at
the end of each week’s lectures. Note we don’t always talk about all of the results of these papers
during lectures. [Optional for UG, Required for PG: Read beyond the essential papers. Perhaps
look at more current papers from a paper discussed in class (perhaps papers which cite the
paper)]. Meet together as a group to discuss these. Questions you may want to consider are:
● What is the technical content of the paper?
● What are the strengths and weaknesses?
● How have they done their analysis?
● What other kinds of datasets could be analysed using these steps?
Choose either one of these papers to follow as an analysis guideline OR choose one of the
ideas posted in the coursework section on QMPLUS. For the first deliverable (described
below) you should think about how this applies to your dataset (see below) and what you expect
to find. You are not expected to have completed the analysis for this deliverable.
Your group should select an existing public dataset or collect a new network dataset. The dataset
chosen or collected must be >200 and can either be collected by your group or chosen from a
public dataset such as those provided from http://snap.stanford.edu/data/, Index of Complex
Networks https://icon.colorado.edu/#!/networks?platform=hootsuite, Network Repository
http://networkrepository.com/ and others. In choosing your dataset bear in mind the papers you
have read, which dataset (not used in the original analysis) would be suitable for a similar kind of
analysis as that which was presented. So for example if you chose the Onnela paper discussed in
week 2 you would need to pick something that had a concept of tie strength.
Discuss your ideas, datasets etc with the lecturers/teaching assistants for the module.
From the 10th of February, all demonstrators will have bookable time to have short chats (see
QMPlus forum).
Deliverable 1: Lightning talks hand-in (Feb 28th), and Lightning Talk Day (March 2nd and
March 6th) - Grade weight: 15%
A lightning talk is a short pitch to articulate a topic in a quick, insightful, and clear manner. For this
coursework, the lightning talk should include the following content (2 to 3 slides):
● title of your study, your names, coursework information and year
● a brief introduction to your dataset
● a very brief presentation of the study in the original dataset paper and/or paper(s) that
have inspired your proposed analysis
● what you plan to do in deliverable #2 in terms of network analysis for your dataset: briefly
describe the proposed analysis, how it compares to analyses from reference paper(s),
what you expect the results will look like
The talk should last no more than 4 minutes so “PRACTICE PRACTICE PRACTICE” is the key.
All members of the group should speak.
What to hand in: Absolute maximum of 6 slides:
● 1 Slide: Title slide (introducing your group members/Topic).
● 2 to 3 slides that your group will use to talk to (see content above)
● 2 supporting slides containing the basic network statistics.
● Presentations should be either PDF slides or PPT. These slides should all be submitted as
one submission.
Lightning Talk Session information:
Times:
Session V1 4pm - 6pm Skeel LT 2nd March.
Session V2 4pm - 6pm Eng 3.25 2nd March.
Session V3 2pm - 3pm Skeel LT 6th March.
Part of the goal of the lightning talk is to give you a chance to explore what some of the other
groups have come up.
NOTE: Presentations will be marked your allocated session. Any groups with no members
present on presentation day will not be graded. Talks will be graded as follows (based both on
the slides and presentation):
[Presentation] Clarity of description of previous work 10%
[Presentation] Suitability of the dataset for the analysis
presented.
15%
[Presentation] Evaluation methodology proposed 15%
[Presentation] Ability to keep to time 5%
[Slides] Style, Writing and clarity of layout
1. Style (/7)
2. Writing (/7)
3. Clarity of Layout (/6)
20%
[Slides] Dataset statistics:
1. The dataset, description, source, visualisation, nodes/edges is
presented.(/10)
2. Appropriate range of statistics presented. (/20)
(Suggested ones are degree distribution, clustering coefficient, modularity,
centrality, but others acceptable)
3. Evidence of these being calculated. (/5)
(Gephi graphs, code snips or screenshot, etc)
35%
All attending members of the project team will receive the same grade, members not present
without a valid EC will receive 0.
Estimated number of study hours for this coursework deliverable including lectures and
seminars: 22.5 hours
Deliverable 2: April 20th - Grade Weight: 15%
Your group should write a short paper (length MAX: 6 pages excluding references) using IEEE
conference format https://www.ieee.org/conferences/publishing/templates.html. All reports should
contain (but are not limited to) sections such as introduction, related work, dataset, approach,
results, conclusions, references. The author listing at the top should detail the contributions of
each author to the project as a whole and suggested percentage division based on that. Your
individual grades will be adjusted according to contribution:
Jane Doe
Problem formulation, algorithm
implementation, report writing.
35%
Bill Smith
Data acquisition and pre-proprossing,
graph drawing, report writing.
25%
Leanne Kains
Running test, comparing algorithms,
tabulating results, report writing.
40%
The results should be commented and justified. It is not sufficient to simply list results; you should
derive conclusions.
What to hand in:
● Your 6 page paper.
● Appendix IF:
○ You implemented your own algorithm (include the code).
○ You collected your data set (include code and details about collection).
Please hand the Appendix in as part of your paper (if you have an appendix you
can go beyond 6 pages). QMPlus will only accept one document as your hand in
and not a zip.
Reports will be graded as:
Introduction and problem definition 15%
Related work 10%
Dataset and algorithm/model description 30%
Results and findings 30%
Style and Writing 15%
Groups should submit to QMPlus the PDF paper as detailed above.
Other information
You are free to use any data analysis tool, e.g. R, Matlab, Gephi, and can even calculate
measurements by yourselves.
Supporting materials:
1. R and Data mining : Yanchang Zhao, Chap 1011, http://www.rdatamining.com/home
2. R language: Computing for Data Analysis https://www.coursera.org/course/compdata
3. iGraph with R:
http://www.r-bloggers.com/network-visualization-in-r-with-the-igraph-package/
4. Gephi : Gephi - The Open Graph Viz Platformhttps://gephi.org/
Network analysis with Gephi: 10:55Gephi Tutorial - How to use Gephi for Network
Analysis
Estimated number of study hours for this coursework deliverable including lectures and
seminars: 22.5 hours