Description
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 fell free and be creative.
– The network should have at least 10 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.
– You should 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, or a network data of which is readily available.
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:
Describe briefly 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 three 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 and interpret the results of your blockmodeling.
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.
