This document discusses a project to visualize large social networks in a meaningful way. It notes that existing social network visualizations only show direct connections and not the broader network. The project aims to address this by using clustering techniques to reduce network complexity. Specifically, it will use both classic clustering to identify social groups and structural clustering to merge similar nodes. The document outlines problems in visualizing large networks and provides an initial solution of downloading profile data and implementing basic classic and structural clustering. It concludes by listing further work needed to study clustering impacts, combine techniques, test algorithms, and develop the user interface.

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.