{"id":11449,"date":"2018-11-07T14:49:13","date_gmt":"2018-11-07T19:49:13","guid":{"rendered":"http:\/\/studentwork.prattsi.org\/infovis\/?p=11449"},"modified":"2019-01-10T23:53:36","modified_gmt":"2019-01-11T04:53:36","slug":"marvel-social-network-visualization","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/marvel-social-network-visualization\/","title":{"rendered":"Marvel Social Network Visualization"},"content":{"rendered":"

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Introduction<\/strong><\/h3>\n

Marvel started in 1939 as Timely Publications, and by the early 1950s, had generally become known as Atlas Comics. The Marvel branding began in 1961, the year that the company launched <\/span>The Fantastic Four<\/span><\/i> and other superhero titles created by Steve Ditko, Stan Lee, Jack Kirby and many others.<\/span><\/p>\n

Marvel counts among its characters such well-known superheroes as Spider-Man, Iron Man, Captain America, Thor, the Hulk, Captain Marvel, Black Panther, Deadpool, Doctor Strange, Wolverine, Daredevil, Ghost Rider and the Punisher, such teams as the Avengers, the X-Men, the Fantastic Four, the Inhumans and the Guardians of the Galaxy, and antagonists including Thanos, Doctor Doom, Apocalypse, Magneto, Red Skull, Green Goblin, Ultron, Doctor Octopus, Loki, Kingpin and Venom. Most of Marvel’s fictional characters operate in a single reality known as the Marvel Universe, with most locations mirroring real-life places; many major characters are based in New York City.<\/span><\/p>\n

This visualization will present an overview of Marvel’s superhero networks. <\/span><\/p>\n

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Materials<\/b><\/h3>\n
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  1. The Marvel Social Network Gephi file<\/span><\/a> \u2013 This network of superheroes was constructed by Cesc Rossell\u00f3, Ricardo Alberich, and Joe Miro from the University of the Balearic Islands. The data was collected by Infochimps and transformed and enhanced by Kai Chang.<\/span><\/li>\n
  2. Gephi<\/span><\/a> \u2013 It is an open-source software for network visualization and analysis. It helps data analysts to intuitively reveal patterns and trends, highlight outliers and tells stories with their data. It uses a 3D render engine to display large graphs in real-time and to speed up the exploration.<\/span><\/li>\n<\/ol>\n

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    Inspiration<\/strong><\/h3>\n

    After choosing the dataset, I found this fan-made visualization which is also about the Marvel networking mining. <\/span>Closeness Centrality and Yifan Hu algorithm is applied to this network. It distances the communities that are independent to the network. \u00a0Main characters, or characters with more connections, have bigger nodes. And node colors indicate different superhero communities.\u00a0<\/span><\/p>\n

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    Link to Graph: https:\/\/www.slideshare.net\/MuthuArasan\/marvel-graph-network-mining<\/p><\/div>\n

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    Method<\/strong><\/h3>\n

    Dataset:<\/strong><\/p>\n

    I found the data set on <\/span>Gefi wiki<\/span><\/a>. I chose it because I\u2019ve personally very interested in the subject. The problem with it is the dataset is too huge that Gephi tends to crash quite often. <\/span><\/p>\n

    Gephi:<\/strong><\/p>\n