Social Network Analysis
Paper details:
Students must select any dataset they wish i.e. they can use a readily available dataset they have found online or they can scrape their own data from any website they wish and conduct a social network analysis to identify local and global patterns, locate influential entities and examine network dynamics. They should construct an appropriate graph with nodes and edges and conduct an analysis using appropriate measures (such as degree centrality, for example) and describe the results obtained. Students may wish to use any programming language, I suggest R with NetSci package, Python with NetworkX package or UCINET to conduct the analysis. The report should contain 5,000 words and all the appropriate code and dataset must also be submitted. Please find the detailed description of the report attached below.
Here is the description of the course if you want to find more information:
https://www.mgmt.ucl.ac.uk/module/msing017-analysis-social-networks
MSING074—Network Analysis
UCL School of Management 2017-2018
Guidelines for Project Report
The report should have 10 pages or less (**excluding** figures, tables and references) in A4 size with 1 inch (2.54 cm) margins in each side. The font should be Times, size 12.
The report should be written **clearly**, **succinctly** and **accurately**. In other words, avoid any non-relevant information, use simple and direct sentence structure, and describe all data, information and illustrations accurately.
The report should contain **all the information** needed for anyone in the field to be able to **replicate** what you did.
The report should follow the following structure and content:
1. Introduction: Here you will describe what you did (the topic) and why you did it (objectives). It includes brief background information about the topic. When reading the introduction, readers should have an idea about your project.
2. Literature Review: Here you will discuss all the **relevant** work that has been done on the topic, and describe how your work is new and/or different.
IMPORTANT: If your project just replicated all or part of existing work, you must clearly state this here and possibly in the introduction. Failure to do so is a major misconduct!
3. Methods:
3.1: Data: Here you will describe clearly all the characteristics of the data you used, including where it comes from (e.g., scrapped from XYZ site indicating the URL; downloaded from where; etc.), what the variables are, what period of time does the data cover, number of cases, etc. You should explain **why** this data is appropriate (in nature and size) for what you are trying to study.
3.2: Network: Here you will explain clearly how you have built the network, i.e., what are the nodes and edges.
3.3: Measures: Here you will describe what measures (e.g., degree centrality) you have used and **why** (i.e., discuss why the chosen measure is appropriate for what you are trying to study)
3.4: Other Methods: If you used other methods (e.g., machine learning, statistical methods, etc.), you should describe what you did and why you used them.
4. Results: Here you should describe and discuss all the findings of your study.
5. Discussions: Here you should discuss what the results of your study mean as well as the insights that they provide and discuss the limitations (if you wish, you may suggest future studies that may result from the limitations of your study).
IMPORTANT: The Project Report should be uploaded in Moodle Turnitin. Together with the Project Report, you must submit the entire final dataset(s) and the final code(s) used in your study so that
MSING074—Network Analysis
UCL School of Management 2017-2018
we can verify your results. **If we run the final code with the final dataset and it doesn’t work, we will not be able to grade your project**. If you used UCINET, you must submit the final dataset (all ##H and ##D files) and all the sequence of commands in UCINET that you used to obtain your results.
If the final dataset is too large (> 40MB) for Turnitin in Moodle, please use the drobox (www.dropbox.com)
