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Project Report
Guidelines You can find some
guidelines for the project report here.
Make sure that you start the report early! Paper Presentation
Guidelines The presentations will
be evaluated based on the quality of the presentation, and the comprehension
of the material covered. The following are some guideline, tips and advice
for preparing your presentation. ·
You
have 20 minutes for the presentation (1 student group) and
25 minutes (2 students group). We will enforce the time limit and
cut you off if you have not completed on time. 10 more
minutes will be allocated for questions. We may randomly pick someone from
the audience to ask a question, so everyone should pay attention. ·
You
should prepare around 20-25 slides, given that a slide takes
around a minute to talk about on average. ·
Break you presentation
into thematic units. The following flow is very common: 1. Motivate
why the problem is important and give a high level idea; 2. Define
clearly the problem; 3. Present
the main idea and the fundamental algorithms; 4. Present
the results (experimental or theoretical or both); 5. Conclusions. ·
The
talk should be self-contained. Do not assume that the audience
has read the paper, or some previous work that you consider known. Define all
the concepts you need and all the notation that you use. Refer only
to related work that you know. ·
Since
the time for the talk is short, you will need to focus on the important parts
of the paper and avoid going through all the details. The goal is to give a
summary of the paper and have a clear message. Just because you read all the
paper it does not mean that you should present everything. At the same time,
you should not skip important information. Focusing on the right part to
present is important since it shows that you understood the paper well. ·
Prepare
the slides carefully. Do not add too much text, and only the math symbols
necessary. Do not use full sentences, but rather keywords and short phrases.
Make sure the slides are readable and not too loaded. Never ever project
parts of the paper pdf. ·
Practice!
Good talks are the result of a lot of practice even if they seem spontaneous
and fun to the audience. Practice the talk several times, and time yourself
to make sure you are within the time bounds. Some fun advice on how
to give a bad talk (and more) here. Projects The list of projects is available here. The assignment is First-Come-First-Serve.
The projects will be done in teams of at most two students. Send an email to
both instructors with the names of the team members, and your top-3 preferred
projects. As soon as we receive your email we will assign you the highest
ranked project that has not already been assign Deliverables and Timeline:
Assignment 2 Due April 6 in class. For this assignment you will create a Jupyter Notebook where you
will experiment with Pagerank and community discovery algorithms. The
notebook will contain the code you have written, the output of your code, and
any commentary you have on your results. You can either write your own code or use implementations
provided by SNAP, NetworkX, or other sources. It is recommended to use
existing libraries. Specify this in your report.
(1)
The
Karate Club graph. You can load this dataset using a built-in NetworkX
command. You can download the communities from here. (2)
The
Books for US politics dataset. (3)
The
Political Blogs dataset. Use the largest WCC for all these graphs. Using these datasets you will answer the following questions: A.
You
will use Personalized Pagerank (PPR) to measure homophily. For a node x, that
belongs to community c, we define the homophily score for x as the total PPR
mass it allocates to the nodes in the community c (excluding itself). Compute
the average homophily score per community and overall and briefly comment on
the results. B.
Apply
two of the algorithms we have seen in class for community detection on the
graphs (for the same number of communities as the ground-truth communities
when possible). Evaluate the algorithms by creating a confusion matrix with
the ground-truth communities. Briefly comment on the results. C.
For
the Karate Club dataset, the ground-truth communities correspond to a split
of the members of a Karate club after a disagreement between the two teachers
of the club. These two teachers are the nodes 1 and 34 in the graph. Use
Personalized Pagerank to assign each node to one of the two teachers.
Evaluate the assignment using the ground truth communities. The assignments should be done individually. Export your
notebook to HTML, and submit both the notebook and the HTML file. Assignment 1 Due March 16 in class. For this assignment you will create a Jupyter Notebook where you
will experiment with network measurements and network generation models. The
notebook will contain the code you have written, the output of your code, and
any commentary you have on your results. You can either write your own code or use implementations
provided by SNAP, NetworkX, or other sources. It is recommended to use
existing libraries. Specify this in your report.
(1) The Wiki-Vote and
the ego-Facebook
graph from the SNAP
dataset repository. (2) An (undirected) Erdos-Renyi random graph. (3) An (undirected) graph generated using preferential
attachment. (4) A graph generated using the forest fire model. The number of nodes of the generated graphs and (when possible)
the (expected) number of edges of each of the synthetically generated graphs
should be the same to one of the Wiki-Vote graph. For these graphs: a. Plot the degree distributions for each graph.
Produce 3 plots (simple distribution, cumulative
distribution, zipf). All plots should be in log-log scale.
(Use the grid option to put all plots per dataset in the same line) b. Report the effective diameter for all graphs. c. Report the
clustering co-efficient for all graphs. Briefly comment on the results. The assignments should be done individually. Export your
notebook to HTML, and submit both the notebook and the HTML file. |