1 / 6

Community Detection in a Large Real-World Social Network

Community Detection in a Large Real-World Social Network. Karsten Steinhaeuser Nitesh V. Chawla DIAL Research Group www.nd.edu/~dial University of Notre Dame. April 1, 2008. Cellular Phone Network. Real social network Represents actual interactions between individuals

orinda
Download Presentation

Community Detection in a Large Real-World Social Network

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Community Detectionin a Large Real-World Social Network Karsten SteinhaeuserNitesh V. Chawla DIAL Research Group www.nd.edu/~dial University of Notre Dame April 1, 2008

  2. Cellular Phone Network • Real social network • Represents actual interactions between individuals • Requires intent to communicate • Network dimensions • 1.3 million nodes (customers) • 1.2 million edges (aggregate of voice and text) • Contains a wealth of data • Communication Links • Customer Demographics • Temporal Data • Spatial Data

  3. Input Graph Weight No Weighting Topology-Based Attribute-Based Weighted Graph Walk Clustering Using Random Walks Co-Association Matrix Combine Agglomerationwith EA Community Structure Community Detectionwith Random Walks

  4. Algorithm Comparison

  5. Experimental Results • Edge weighting based on topology • CCS = clustering coefficient similarity • CNS = common neighbor similarity • Real edge weights • Call frequency • Call duration • NAS = edgeweighting basedon node attributes

  6. Future Work • Incorporate network dynamics • Spatial data • Temporal data

More Related