Visualizing Networks with Gephi

In April of 2015, Facebook removed the ability to find personal network data. Also, new Netvizz privacy policies mean that any data pulled uses a hash strings instead of actual names. The tutorial video is no longer able to be followed due to these changes, however I’ve changed a few things and completed the assignment as best as currently able.

Instead of pulling a graph from my personal network, I used data from the group “Shadle Park High School Grads” and created a Grephi graph using post data from the users. One immediate trend I noticed in the data I pulled was the stark lack of outliers. In a high school reunion type scenario, users are only present if they want to be there, and they all have a common bond. This interconnectivity is apparent when the graph is viewed in any layout. However, certain subgroups are readily visible as well. While the anonymized data that Netvizz will only provide has a lack of context, it’s easy to assume that these nodes of users are grouped by graduating year, circles of friends, shared interest, or other categories.

This is becoming more common in the current day, as the wild west days of the internet are going and the monetization of every aspect is becoming the norm. Google has unseen control over what you can see, and Facebook is able to obscure sponsored content as a friendly face. When a culture is fueled by capitalism, innovation skyrockets, but dollars are always the bottom line.

The competition between web companies is not about public image, not about customer service, and not about happiness, competition is for profit. While the drive for profit can benefit the consumer when the consumer has a choice, it is not so when a company has a monopoly, like Google and Facebook do. They are no longer just a search engine and a social media site, they have integrated into so many aspects of our lives that we feel we have no escape.

How can we tell when a company has our best interest at heart? When they begin to hide public data, when they restrict useful tools, when they obscure their practices, then it becomes a hard judgement to make. Data and analysis are important tools for everyone to use, just as our guest lecture had mentioned, data analysis helps us see the world for what it is and what it could be. However, with every useful tool, a price must be paid. For example, the lectures showed us a site called jeffrey’s Image Metadata Viewer. This is a powerful tool to gather data from images out there on the web. However, Facebook does not allow this luxury. Mostly because of supply and demand. Metadata is like a gold mine.

All in all, it would be interesting to see the data from a few years ago, before Facebook and Netvizz stopped providing the tools to completely visualize the online network. However, it is in their best interest to save data for those willing to pay. As Doctorow says in Homeland, “As long as the algorithms involved are kept secret. Without knowing what Google actually does when it ranks sites, we cannot assess when it is acting in good faith to help users, and when it is biasing results to favor its own commercial interests. “

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