Reference no: EM131016628
Construct a social network from the real-world data, perform some simple network analyses using Gephi, and interpret the results.
Social Network Application and AnalyticsIn this assignment, you will be asked to construct a social network from the real-world data, perform some simple network analyses using Gephi, and interpret the results.
1. Download the Twitter data (.CSV) from the course Blackboard website that we collected for you based on the list of keywords that you had provided in your previous assignment deliverable. A description of the data file is provided to help you understand the meanings of columns contained in the file.
2. Construct a graph of a social network, as shown in the sample below. The network should consist of at least 15 nodes. To this end, you need to:
(a) determine what the nodes and links in the graph represent;
(b) identify the nodes, and
(c) extract the relationships/links between the nodes.
For instance, the nodes could be used to represent tweeter users, and links represent one or a combination of the following types of relationships in tweeter: retweet, reply- to, and follower. If necessary, extract additional tweets from Twitter to construct the network. In addition, you may change the layout of some nodes to improve the graph presentation. You are allowed to create shorthand notations for the nodes and use those notations in the graph representation. If shorthand notations are used, you need to explain them in a note area of your social network graph.
Figure 1. Sample graph of a social network
3. Determine whether the graph/network is directed or undirected. If directed, transform it into undirected graph/network.
4. Prepare an input file to Gephi based on the above undirected graph/network. You are allowed to choose any input format for the tool (e.g., .CSV). Avoid having text with spaces in between while inputting into Gephi.
5. Use appropriate Gephi analysis function(s) to compute some basic network measures for all nodes in the network, including network diameter, degree centrality, betweeness centrality, closeness centrality, page rank, hits, connected components and graph density.
In summary, your deliverable will contain the following items, which can be submitted as a single file or a zipped package.
(a) a social network graph; and shorthand notations for the nodes (if needed)
(b) the types of information that you have used in identifying the relationships (e.g., retweet)
(c) transformed undirected graph/network in a matrix form
(d) an input file to Gephi
(e) the computation results of basic network measures (screenshots are fine) and
(f) the nodes that have the highest degree centrality, highest betweenness centrality, and highest closeness centrality in the network, separately.
Attachment:- Staples.rar
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