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CLIQUE FINDER

CLIQUE FINDER. By Ryan Lange, Thomas Dvornik, Wesley Hamilton, and Bill Hess. PROBLEM -. How do friends form groups based on their Facebook activity or information? Cluster friends based on their profile information and the relationships they share with other users on your friends list

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CLIQUE FINDER

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  1. CLIQUE FINDER By Ryan Lange, Thomas Dvornik, Wesley Hamilton, and Bill Hess

  2. PROBLEM - • How do friends form groups based on their Facebook activity or information? • Cluster friends based on their profile information and the relationships they share with other users on your friends list • Form cliques or groups of people within your friend’s list that may not have been considered before.

  3. SOLUTION • Facebook API • Clustering • Distance Metric • Home town, religion, images, shared friends, etc. • Bottom-up vs Top-down • Hierarchical • Max flow • Testing • Fake dataset • Varying sizes

  4. DATA SET • Some Data Restrictions • Cannot contain functionality that requests or collects Facebook usernames or passwords from any user; • Cannot store data received by Facebook for more than 24 hours • Cannot modify, rent, lease, loan, sell, distribute, or redistribute data to another part • Collectable Data • User’s Personal Data - gender, age, networks, relationship status, hometown, religious, status feed, etc • User’s Visible Data - wall post, comments, messages, notes, pages, notifications, events, groups, links, streams, friends, pictures, videos, tags, etc • Friend’s personal and visible data • All pictures that the user is tagged in

  5. DATA ADDS UP FAST • Testing Set – • About 7000 pieces of personal data • 40000 wall post • 350 photos, about • 1600 tags • tons of other miscellaneous data; such as status, events, links, notes, etc. • Data set includes at least 10 heavy users - • 70000 pieces of personal data • 400000 wall post • 3500 photos • 16000 tags • and much more miscellaneous data..

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