Social and technological network

Projects

Final Submission deadline: November 15, 4pm. Each student must submit an Individual report.
- Submission guidelines -- what to submit.
- how to submit
Tips and guidelines for the project and report

For example projects, see past projects from the Stanford course..

Project submission guidelines -- what to submit

The submission should include:

Report Individual report. pdf, 3 pages max. Optionally with and appendix that will not be marked.
Code The code you have implemented, along with scripts that run the code and plot the results.
Dataset The dataset. The actual data may be too large to submit, in which case put your data in a publicly readable folder and make sure your program reads from there.
(Optionally)Examples Example of running the different experiments to get the important plots. This should be a pdf that shows the commands and the corresponding plots for a small dataset that can be processed easily. If you are using ipython notebook, simply printing the notebook to pdf after creating all the plots should do the trick.
(Optionally) The ipython notebook if you are using ipython.

More details on these elements are given below.

How to submit.

Put all the files as described above in a folder named

<yourID>_coursework

on DICE, where <yourID> is your university student id.

on DICE, run the command:

submit stn cw1 <yourID>_coursework

It is a good idea to try the submission early to make sure that it is working for you. You can resubmit upto the deadline.

If you are using virtual dice, you should know that in the past virtual dice has had problems executing submissions.

Parts of the submission

Report

The report should be a 3 pages pdf document. The list of references can be outside these 3 pages. Shorter and concise is better! Make sure to describe everything in your own words. Remember to put a suiatble title to your project and mention your team. The report should include:

Problem statement. Clearly define the problem you are solving. This can be an optimization problem, a hypothesis to validate or invalidate etc. It can be different from what you said in the proposal. A (correct) mathematical formulation will be appreciated. Keep in mind that the description should be readable by anyone without specialized knowledge. Explain very briefly the importance of the problem. State why the problem is challenging.
Related work. Mention any relevant papers and state what problem they are solving. Don't describe all the details, just clarify how your work is different.
Your solution/approach. Describe in details your idea for solving the problem. Where possible, show why it is different from previous work. USe examples. Time may not permit you do everything, in which case explain what you are presenting here and what can be done later.
Results. Describe your results. Divide into algorithms, analysis, experiments etc as suitable. See how computer science papers are uaually divided. Give plots, tables that show your results. Discuss them. You need to explain the results and their importance to us. We were not there during the work, so we cannot understand things that you do not explain. Discuss what else can be done on this topic.

Appendix. You can attach an appendix of up to 3 pages with any additional plots, results and proofs that might be important. This can include results from your teammate that may be relevant to your discussions. You will not be marked on the appendix, these are supporting material to validate what you say in the report. You can cite material in the appendix from your main report, but keep in mind that we may not read it. So the main report has to be understandable on its own.

The code

This should be readable and well commented. The generally good way to structure it is to write code for important elements: classes, functions etc, and then to call them from different scripts, passing suitable arguments like data file, parameter values etc. ipython notebook may be good for this.

Examples

We would like to sometimes run your algorithm to see that it works. So if possible, provide a guideline pdf. This can simply consist of what command to run to which plot. And any comment you would like to include. These examples should be in terms of some small datasets, for example, those you use for testing while writing code.

Including an ipython notebook can be the easy thing to do if you are using ipython.

This should also contain clear instructions on how to run your code on a different dataset if needed.

Ideally, your code should run on DICE. (you don't have to work on it, just try to make sure the code runs.) If it is not possible to run it on DICE, please provide clear instructions on how we may be able to run it on other computers.

Dataset

Submitting the dataset may be impractical if it is larger than a few megabytes. Please put it in a folder and make the folder readable by all on DICE. This can be done by running the following command from the parent directory:

chmod -R a+r <folder_name>

In your submitted code and example codes above, use the absolute full path to the dataset folder on dice. You can find the path to any directory by running

pwd

from inside the directory. This way, we will be able to run your code on the data without you having to submit the data. Test that this is working before submitting.

The dataset can also contain the small sample datasets for the examples above, and any cleaned/modified.reformatted versions of data you are using.

If your main dataset is too large for storing on your account, please provide the link to the original dataset, or put it on a cloud service and provide that link so that we can download.

Project and report tips

You will be marked on trying new ideas, justifying them and explaining clearly. Ideas may not always work out well, but your should be able to justify your decisions and ideas.

Group work:

Everyone should submit their own report. Some parts, such as problem description and related works can be similar, but written in your own words and with reference to your work and results.
The rest is expected to be your own. This can be achieved by different team members being responsible for different parts.
If the project has significant theoretical and experimental sides, it may be reasonable for one person to do each.
Your submission is your responsibility; make sure that you have a secure plan and will be able to submit something for your part.
You are free to cite your teammate's work in your discussion to explain your work and point out interesting comparisons. You can also include some of their plots, results etc in your appendix if it makes easier to write, but you must state that these are your teammate's work.
Brainstorm in group, but do your own project. Do not expect the group to tell you what to do. Use the group to discuss specific issues and brainstorm ideas. Then go and work on your projects on your own.
Give credit/citation when you use an idea from a different person (inside or outside team) or source like a book or paper. You will not lose marks -- your achievement is in recognising the usefullness and adapting the idea.

Writing:

Look at papers. Your report should look like that. Having an "Abstract" summarizing the problem and results is often useful.
Remember that you will not be marked on what you did. You will be marked on what you write in the report.
At the beginning, explain clearly what problem you are solving, why it is important and what you have found. Yes, summarise the results right at start and don't keep it for the end. What is interesting? What is new? Why should someone read your paper?
Avoid statements of the type "I did X." The reader is not interested in knowing what you have spent your time on. Instead, present facts, ideas that the reader may find useful.
Look at it from the reader’s point of view. They do not care (at least initially) of exactly what all you did. You have to give them an initial statement of what they can gain from your work. Then, if they are interested, they will read the details to learn exactly how to get that result and what it means.
The same applies to every bit of writing -- sections, paragraphs etc. Start with a summary of what you want to say.
Writing must be clear and easily understandable. Think if all your classmates will understand what you write. If not, explain what is necessary to appreciate your work. Present them in the right order.
Remember that the marker does not know what you are thinking and does not know all the techniques/concepts you may be using. So you need to explain anything you use.
- These explanations must be short. Often a single line suffices.
After you write, read it and make sure it is readable and understandable to others. If we cannot follow your report, we cannot give you marks.
Start writing NOW. Start putting plots, results into a file. Jot down descriptions. You wil find it much easier to fill in the rest and make a good report out of it.
Do not try to put everything in the report. You have 3 pages. Choose the most important ideas and results, and present them nicely so that the reader appreciates them. Some other things you can put put in appendix with shorter descriptions.
Do not keep report writing for the last day. Again: you will be marked on the report. So write it soon. I suggest that you finish the report a day or two before the deadline. Then you can go back to do few more experiments, add in any further interesting results and update the report.

Project management.

At the beginning, it is often useful to experiment with small datasets (like parts of a big one) or even artifical ones. This is useful in making sure that your code works as expected, and get intuition of where it is working and where not. You can also easily plot and visualize small graphs. These are useful examples in developing your ideas as well as explaning them in the report.
Things will not always work out the way you expected.
You may find yourself short of time. Do not panic! A common mistake is to stick too rigidly to the plan. Think what you can do instead of the original plan.
May be you can run your program on a smaller dataset. May be you can simplify the algorithm so that it does not do exactly the same thing or does not work on all types of data, but does work on some types of data. May be it is solving a slightly different problem from the original one. May be the reason it does not work is an interesting observation in itself.
Instead of worrying that you may not be able to do it, think what you can do. Often, interesting things crop up if you spend a bit of thought. If you keep notes of ideas/possibilities from start, some of them can be useful in a crunch. Your final goal is to submit some kind of an interesting report.

Comparisons.

A useful way to make your report interesting is comparisons. A plot or result probably cannot show your idea to be good or bad on its own. Good or bad are relative -- you need something to compare with.
This "something" may be an existing method; may be a simple, naive way of solving the problem (brute force, random, results known from other sources etc) or may be comparison between two different ideas you have, or may be comparison between results on two different types of networks/data.
A comparison gives you things to discuss and explain (what is the difference, why it exists etc), and thus make your work interesting. So think about what comparisons you can show.

Project Plan/proposal

Plan/Proposal deadline: October 26, any time.
If you are in a group, you can submit one proposal for the group. Submission instructions below.

The purpose of this plan is to make sure that you are prepared for the project and have some ideas to go about doing it. It is possible to adjust your plan as you do the project. The proposal is not marked.

Submit a half-page (not more) pdf document for your group. The document should contain:

Team. Mention your team in the document: names and student ids.
Problem statement or hypthesis. What you have now is a general topic area. A "problem statement" is something more preceise that can be unambiguously checked if you have achieved. For example, "influcence maximization" is a topic, but is vague until we state the exact problem. In class, we defined a particular influence maximization problem, by defining the independent activation model and the "problem statement" was to design an algorithm (for this model) that achieves a property: an approximation of the maximum influence in polynomial time.

In your project, a problem statement can be to design an algorithm that satisfies some property X. If you are doing an experiment or data exploration oriented project, then the problem statment should consist of a "hypothesis" to be tested. A hypothesis can be something about the properties of the data (and network defined from it), in terms of, say communities, their properties, connected compoonents, shortest paths etc. In the final report, you should be able to say if the hypothesis was validated or not. The problem statement should not be something trivial, like what is already generally known.

The problem statement can be fine tuned as you do the project. So it is not that you absolutely must achieve what you set out here. But it is important to have a credible problem statement now so that your project has a clear direction.
Importance. State why you think the problem is interesting and relevant. If you solve the problem, how can someone use that result?
The relevant dataset. State which dataset and attributes of the data you will use to solve the problem. Why the dataset is sufficient and has all the information needed for your project. This means that you must have found, opened and started to use the dataset by this time.
Related work. Which other papers have already addressed the same/similar problem? And optionally, how will your work be different? (you do not need to write a complete survey. Check if there are papers that are close to your problem statement figure out their topic.)
Preliminary ideas. Highlight a few techniques that you are planning to explore in the project. The more original the better! Don't be shy to try whacky ideas if you can justify them!
Evaluation and Baselines. How would you show that your solution is good and your observations are meaningful? Usually this is done by comparing with "baselines" -- existing simple methods or standard networks. How would you do evaluation? What are your baselines?
Schedule/Timeline. What are the intermediate stages for your project? When do you expect to finish each of those?

Additional consideration: Teamwork: How would you split the work in the team? Who will submit what in final version? Plan this from now. Your marking will be separate. You can include this in the plan if you wish.

Additional consideration: Computation time. How long will it take to run things on the data? Of course, you cannot know this exactly, but try some simple computations on the data so that you get some idea. If the computation is too time consuming for a large dataset you are using, how would you adapt your approach?

It may not be possible to answer all the questions above perfectly at this stage. But you should have answers to the problem statement, dataset and ideas questions. Do try to answer the rest to save issues later on.

Submission instruction

On Piazza, post a note in the project folder, with the proposal as a pdf attachment. You are free to post it publicly, in which case other classmates can see the discussion.

Put an appropriate title for the note. In the body of the post, mention your team, and any specific questions or discussion points you have. We will try to answer them.