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Midterm
 

Midterm

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    Midterm Midterm Presentation Transcript

    • A large scale social network visualization Rashid Bhamjee Supervised by Dr. Benoit Gaudin
    • Project Motivation
      • Many applications exist to provide a visualization of how a person and their friends are connected.
      • None provide a visualization of their greater network.
      • Provide a visual representation of how a person is connected to their friends and how their friends are connected to each other.
    • Problems
      • An extremely large amount of data to visualize.
      • A standard graph drawing is too cluttered making it difficult to visualize meaningful information.
    • A Solution
      • Use clustering techniques to reduce the size of the graph.
      • Two types of clustering techniques: “Classic” clustering, structural clustering.
      • Use a combination of both techniques
    • Classic Clustering
      • Group strongly connected people together.
      • Identifies social cliques within the network.
    • Structural Clustering
      • Merge nodes with the same connections to form a single node.
      • In the case of social networks nodes will rarely be similar; a metric must be used.
    • Implemented
      • Downloaded HTML and parsed profile data and friend relations into a database.
      • ~7,500 nodes
      • ~750,000 edges
    • What’s Left To Do...
      • Study:
        • Impact of each clustering techniques.
        • Combine different types of clustering.
      • Implementation:
        • Several clustering algorithms.
        • Visual interface.
      • Experiment on the usability of the application.