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<\/a><\/p>\nFigure 1, My LinkedIn Network, as visualized using Socilab (www.socilab.com\/home)<\/a><\/p>\n Socilab did provide network analytics (Figure 2), showing absolute size, effective network size, as well as other measures, to<\/p>\n highlight areas where more connections can be made, or a network can be strengthened.<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Visualization #2 was more along the lines of what I was producing once I started working with Gephi datasets. This<\/p>\n graph came from networkrespository.com, a site with multiple data sets for exploration and analysis. It seemed to be closer in<\/p>\n line with the examples in the readings and in class, as it had more connections and stronger ties among the nodes.<\/p>\n <\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Visualization #3 was the closest to the final product from the Gephi lab session. Again, though the focus of Figure 4<\/p>\n (Visualization #3) \u00a0is different than the end result,it provided a representation of what I was looking to do.<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>To create the final product, I used a dataset from Github<\/a> focusing on word adjacencies in the novel David Copperfield. I<\/p>\n settled on that dataset after trying out various other datasets from other sources (such as Networkrepository.com) that did not<\/p>\n work out. One set of data had what looked like properly formatted data (in CSV), but upon trying to import it into Gephi, the<\/p>\n file lacked the appropriate set up (nodes and edges), so it was not usable for visualization. I had tried locating alternative<\/p>\n sources in advance of the lab, but ran into issues such as incompatible file formats and dead links. In the interest of time and<\/p>\n efficiency, the readily-available and importable data worked best for creating a visualization.<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>To create the visualization, I used a dataset from Github, and imported it into Gephi. I had plans to look at other<\/p>\n datasets, but as mentioned above, they were not usable for this exercise. The set used seemed like it would present well and was<\/p>\n not overly large (smaller numbers of edges and nodes) or complex once visualized in Gephi (such as the airline data set). The<\/p>\n end result did look different than what was in the program workspace, but through making adjustments for layout, statistics,<\/p>\n and other factors, it created a usable product.<\/p>\n Working with the program had its challenges, though. The output would not show up in the program, making it<\/p>\n necessary to save the current version, export it, then open the result. Had that not happened, I may have been able to adjust<\/p>\n certain things such as color, with a bit more ease.<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>Using the Github dataset, Figure 5 (below) shows that the word \u201clittle\u201d appears at the center of the \u2018universe\u2019 in the<\/p>\n visualization, and is by far the largest node. Other words also appear frequently (\u201cother\u201d, \u201cold\u201d, \u201cgood\u201d), and were connected to<\/p>\n multiple other words throughout the diagram. Though it was not the dataset I intended to use, the end result succeeded in<\/p>\n visually presenting the networks concept.<\/p>\n While I had a workable dataset and a final product, a brief discussion of ideas explored, but not used follows.<\/p>\n Facebook did not pan out because of changes made to the application program interface (API). Previously users could<\/p>\n map their own networks and their friends would appear as well; after the change<\/a> in 2015, users would only be able to show their<\/p>\n own activity. For a user to show their friends on a network, they (the friends) would need to allow Facebook to access their<\/p>\n connections as well. I did find instructions for building such a visualization, but it seemed rather time-consuming, when other<\/p>\n datasets were readily available on other sources.<\/p>\n I was able to plot my LinkedIn network using Socilab (Figure 1). In terms of answering the \u201cwhat would this look like\u201d<\/p>\n question, it was useful, as I had a diagram to see and evaluate. The output had its limits, as it just reflected my connections, not<\/p>\n the connections of people to who I am linked. From the output, I saw that many of my connections are isolated. Additionally,<\/p>\n the clusters of people that appeared correlated to various parts of my life (work, college, other organizations of which I am or<\/p>\n was a part).<\/p>\n As to future directions, relationships between certain words in the novel could be explored and graphically represented.<\/p>\n The layout could be changed to emphasize or de-emphasize certain nodes, among other areas.<\/p>\n Having a somewhat better handle on Gephi now, more complex data sets could be explored and visualized. Also, having<\/p>\n a somewhat firmer understanding of how to work with formatting data, other visualizations can be created, more in line with<\/p>\n my interests, such as a network of characters from Absolutely Fabulous, for example.<\/p>\n <\/p>\n Sources and Figures<\/p>\n Facebook API change,\u00a0 http:\/\/www.kdnuggets.com\/2015\/06\/visualize-facebook-network.html<\/a><\/p>\n Figure 1, My LinkedIn Network, as visualized using Socilab (www.socilab.com\/home)<\/a><\/p>\n Figure 2, Socilab LinkedIn network analytics (partial)<\/p>\n Figure 3, Visualization #2, from www.networkrepository.com\/web-pollblogs.php<\/a><\/p>\n<\/a>Figure 2, Socilab LinkedIn network analytics (partial)<\/p>\n<\/a>Figure 3, Visualization #2, from www.networkrepository.com\/web-pollblogs.php<\/a><\/p>\n<\/a>Figure 4, Visualization #3, from https:\/\/flowingdata.com\/2010\/02\/26\/news-topics-as-social-network<\/a><\/p>\n<\/a>Figure 5, Output from Gephi lab session (reduced size)<\/p>\n