Midterm
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Midterm Presentation Transcript

  • 1. A large scale social network visualization Rashid Bhamjee Supervised by Dr. Benoit Gaudin
  • 2. 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.
  • 3. Problems
    • An extremely large amount of data to visualize.
    • A standard graph drawing is too cluttered making it difficult to visualize meaningful information.
  • 4. 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
  • 5. Classic Clustering
    • Group strongly connected people together.
    • Identifies social cliques within the network.
  • 6. 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.
  • 7. Implemented
    • Downloaded HTML and parsed profile data and friend relations into a database.
    • ~7,500 nodes
    • ~750,000 edges
  • 8. 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.