Network datasets analysis using python
MATPMD2 Assignment 2020: Network datasets analysis using python
This assignment is worth 50% of the overall grade for this half module.
Assignment details will be released at 10.00am Monday 30th November 2020 on Canvas.
Submission date is 5.00pm 23rd December 2020 via Turnitin on Canvas.
Task: Network datasets analysis using python
Analyse a real network and write up the results. You can use whatever software you are most comfortable with, e.g., Python or R.
Choose one of the networks datasets from a suitable online resource (e.g., such as http://networkrepository.com/) as the focus for your analysis, bearing in mind that you should select one which has some inherent interest to you which would allow you to generate some questions of interest, and also allow you to answer these questions using network analysis techniques we have covered in this module. Select a dataset that contains between 100-1000 nodes, as this will provide sufficient complexity to your network, as well as allow you to execute any code analysis within a reasonable runtime.
The write up must be comprised of the following sections with grade weights on each section shown, and with a word limit of 3000 words, +/- 10% allowance. Any reports that do not meet the word count allowance may be penalised.
Provide some background to the network data set you have selected. What is interesting about this data set? Where was it available? What questions do you wish to answer using this network? (Note – not all of the network datasets on this website have descriptive summaries. Select a dataset which has a name and/or description which allows you to provide some level of basic context to the interpretation of the results of your analysis.)
What are the nodes and edges in this network? What network metrics will you try to calculate for this data? Include your code as an appendix to your report (any included code does not contribute to your word count) or upload your code as separate files along with your completed assignment.
Provide appropriate results for the questions you posed in your introduction, as well as appropriate network visualisations that complement your results/analyses.
What did your analysis tell you? Could you answer your questions, and if so, what were the answers? Did you encounter any problems dealing with a large data set? What were they? Are there things you could have done to avoid them? Was the data detailed enough? Was it of good enough quality? Was the data set of an appropriate size?
Network data: http://networkrepository.com/
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