{"id":17414,"date":"2019-11-13T12:08:16","date_gmt":"2019-11-13T17:08:16","guid":{"rendered":"http:\/\/studentwork.prattsi.org\/infovis\/?p=17414"},"modified":"2019-11-13T12:09:19","modified_gmt":"2019-11-13T17:09:19","slug":"marvel-cinematic-universe-phase-1","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/marvel-cinematic-universe-phase-1\/","title":{"rendered":"Marvel Cinematic Universe Phase 1 : Most Influencial Characters"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

The Marvel Universe, a fictional universe with a great number of characters that are all connected in some way. The universe has more than 10,000 characters. The focus of this visualization will be on phase 1 of the Marvel Universe(2008 – 2012). The goal is to find the most connected characters in this time period.<\/p>\n\n\n\n

Inspiration<\/h4>\n\n\n\n

My inspiration for this graph is the article ‘Shakespearean tragedies visualized through character interactions’. The visualizations have a clear goal. They want to identify if characters are closely connected. They also want to see if there is a pattern in the structure.<\/p>\n\n\n\n

\"\"
Fig. 1 Inspiration: Shakespearean tragedies visualized through character interactions<\/figcaption><\/figure>\n\n\n\n

Materials Used<\/h4>\n\n\n\n

Tools<\/h6>\n\n\n\n

Gephi<\/a><\/strong>\u00a0An open-source software used to create and analyze network data visualization<\/p>\n\n\n\n

Microsoft Excel<\/a><\/strong>\u00a0To format the data<\/p>\n\n\n\n

Datasets<\/h6>\n\n\n\n

The dataset used was taken from\u00a0http:\/\/www.casos.cs.cmu.edu\/tools\/datasets\/internal\/index.php#marvel<\/a><\/p>\n\n\n\n

Methodology<\/h2>\n\n\n\n

To understand the data, I opened it in Spreadsheets. The data was neatly organized. It was a directed network. (Fig 2) <\/p>\n\n\n\n

I removed multiple columns with unnecessary details. The data included the following connections:<\/p>\n\n\n\n

  • Agent to agent<\/li>
  • Agent to location <\/li>
  • Agent to company<\/li>
  • Company to company <\/li><\/ul>\n\n\n\n
    \"\"
    Fig 2. Initial Data <\/figcaption><\/figure>\n\n\n\n

    I only wanted Agent to agent connection so I removed rows with other types of connections. Since it was just agent to agent connection, I removed columns with data to identify the type of connection. After deleting columns with data about location and companies. I ended up with a very simple data(Fig 3).<\/p>\n\n\n\n

    \"\"
    Fig 3. After formating data<\/figcaption><\/figure>\n\n\n\n

    I imported my data into Gephi. The first view on the network seems chaotic and I was not able to interpret anything. I realized I need to to add proper labels and need to need to change the layout.<\/p>\n\n\n\n

    \"\"
    Fig. 4 Initial Graph<\/figcaption><\/figure>\n\n\n\n

    After adjusting ranking, showing labels, adding color and layout, the network was looking much better. It was very easy to interpret the information and find out the most connected characters. <\/p>\n\n\n\n

    \"\"
    Fig 5<\/figcaption><\/figure>\n\n\n\n

    To visually improve the graph, I wanted to make the names of characters more readable. I tried removing edges but it was making it hard to understand the connection. I also tried ‘Tag Cloud’ present but it was adding ambiguity to the network. I removed the stroke around the nodes and change the colors to lighter shades.<\/p>\n\n\n\n

    \"\"
    Fig 6. The final network graph<\/figcaption><\/figure>\n\n\n\n

    Results & Interpretations<\/h2>\n\n\n\n

    Some important stats from the visualization are:<\/p>\n\n\n\n

    • Average Degree: 5.891<\/li>
    • Diameter: 5<\/li>
    • Density: 0.109<\/li>
    • Modularity: 0.536<\/li><\/ul>\n\n\n\n

      I was expecting similar results from the network. Tony Stark and Steve Rogers are the most influentials characters in the universe in all phases. I was not expecting Loki to be one of the most connected characters in phase 1. Overall it was a nice and simple network to show the connection between characters in the universe.<\/p>\n\n\n\n

      Reflection <\/h2>\n\n\n\n

      I spent half of the time understanding the tool and I was not able to work more extensively with the data. Once I got the good command on the tool, the lab time was over and I didn’t get a chance to work on it. The tool can be frustrating but I believe we can do amazing things with the networks. I am happy that I was able to create a nice meaningful graph. I achieved the goal by easily identifying the most connected characters.<\/p>\n\n\n\n

      Future Direction<\/h2>\n\n\n\n

      I enjoyed working with Marvel’s data. For the future, I would spend more time on this graph and make it look more readable and visually appealing. Since I also have the data about the location and company of the agents. I want to explore the connections between locations and the agents. It will be interesting how many agents have been to one location. <\/p>\n","protected":false},"excerpt":{"rendered":"

      The Marvel Universe, a fictional universe with a great number of characters that are all connected in some way. The universe has more than 10,000 characters. The focus of this visualization will be on phase 1 of the Marvel Universe(2008 – 2012). The goal is to find the most connected characters in this time period.…<\/p>\n","protected":false},"author":572,"featured_media":17439,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[149,342],"tags":[],"coauthors":[472],"class_list":["post-17414","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-labs","category-networks"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2019\/11\/DDgDw8e.jpg?fit=1920%2C1080&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-4wS","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/17414","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/users\/572"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=17414"}],"version-history":[{"count":3,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/17414\/revisions"}],"predecessor-version":[{"id":17442,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/17414\/revisions\/17442"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/17439"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=17414"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=17414"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=17414"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=17414"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}