SOCS0081--Social Networks, Assessment 1

INSTRUCTIONS:

- The assessment is due on Mon Feb 24th 2025, 1pm and shall be submitted via Moodle.

- Late submission results in penalties, see: https://www.ucl.ac.uk/academic-manual/chapters/chapter-4-assessment-framework-taught-programmes/section-3-module-assessment#3.12. There is no exception to late submission penalties, unless an extenuating circumstances application has been successfully made.

- Submit on Moodle a single document that includes the main body of your report and any tables and figures you may use in your report. Any R code you use to produce your results should be given in an appendix. If you use some other software than R, do include any code you have used with details of the software you used.

- Word limit is 1,500. This excludes tables, figures, table and figure legends/captions, references and the appendix, but includes footnotes and endnotes. Exceeding this limit will result in penalties.

- At the end of your essay include the number of words of your report, excluding the tables, figures, table and figure legends, references (if you used any), and the appendix with R code.

- If you use any reference in your report, list full bibliographic details at the end of your report. Any referencing style. (ASA, APA, Harvard, Chicago etc.) is fine, provided that the style. is used consistently.

- The coursework will be assessed against the criteria set in the UCL UG-ESSAY GRADING SCHEME, a pdf of which could be found on the programme handbooks. In addition to those general guidelines, further specific factors will affect the marks: correctness of the solutions and interpretations of results, clarity of arguments, rigour in presenting and analysing the network, creativity in your approach, and the ability to demonstrate that key concepts treated in the course are understood well.

- This is an assessed piece of coursework for the SOCS0081 module; collaboration and/or discussion of the assessment with anyone is strictly prohibited. The rules for plagiarism apply and any cases of suspected plagiarism of published work or the work of classmates will be taken seriously.

- This assessment is a Category 2 assessment which means that GenAI tools can only be used in an assistive role. In particular, you can use tools like ChatGPT or CoPilot to assist with R coding and proof-reading, but the data collection, analysis and write-up should be your own.

In this first summative assessment, we ask you to present and study a network that you will create yourself. You may draw upon your personal environment (friends, family, colleagues, organisations that you know/have been a member of ...) to create a network. You may rely on your memory in creating this network. Alternatively, you can collect secondary data on a network that you may or may not be a member of. For example, you can scrap the web, use secondary sources, books, movies, documents, and so on. But do not collect primary data (for example, do not ask directly to people/strangers about their ties), because collecting primary data is a sensitive issue and may require an ethnical review. Below are some constraints on your network. Apart from these constraints feel free and be creative.

- The network should have at least 25 nodes. There is no upper limit on the number of nodes.

- The network should not be fully connected (that is, not all nodes should have ties with all other nodes). Otherwise, it will be a rather trivial network to study.

 - Do not include any personal data in presenting and analysing the network (that is, use pseudonyms or anonymous IDs for the nodes in the network, we don’t want to violate GDPR). If the network you study is freely available in the public domain (celebrities, fictional characters, sportspeople, firms, fictional characters...) you may use real names.

- Any type of network discussed in class is allowed (directed, undirected, weighted, unweighted, bipartite, one-mode, signed, unsigned ...)

- Nodes in the network could be of any type (people, organisations, companies, book characters, ...)

- The network should be original, that is, it should not be a network studied previously by other scholars as a network, or a network data of which is readily available.

For more tips please look at the SNA Cookbook file on Moodle.

Based on your network write a short (1,500 words) report. Your report should discuss at a minimum the items given below. You may comment on additional properties of your network once you cover all items below. You may write a single report discussing all items. You may also structure your report in four parts corresponding to the four groups of items below. Note that the interpretation of a particular network measure is as important as correctly calculating and reporting the measure. So, make sure to include an interpretation of the network measures you report.

A: Description of your network:

Briefly describe your network. What/who are the nodes? What do the edges represent? What type of a network is it (i.e. directed, undirected, ...)? How did you collect the network data (i.e. is it from memory, if it is based on secondary data how did you collect these data ...)?

B: Characteristics of the network and the nodes:

What is the density and diameter in your network? Apply at least four measures of centrality to study the importance of the nodes in your network. Report the values of these centrality scores for the most central four or five nodes. Interpret these centrality measures. Based on these centrality scores who are the most important two or three nodes in your network and why? Comment on how centralized your network is.

C: Characteristics of groups of nodes:

Does your network have any cliques? Describe the k-cores of your network. Are there any structurally equivalent nodes in your network? Run a formal blockmodeling, comment on any nodes that look structurally equivalent to you. Interpret the results of your analyses.

D: Characteristics of the edges:

Study the transitivity of the network by reporting and interpreting the global and local clustering coefficients. If it is a directed network, also calculate and interpret the reciprocity of the network. If it is a signed network, comment on whether your network is structurally balanced. Interpret your results.