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Email Alias Detection Using Social Network Analysis 

Email Alias Detection Using Social Network Analysis . Ralf Holzer, Bradley Malin, Latanya Sweeney LinkKDD 2005 Advisor: Dr. Koh Jia-Ling Reporter: Che-Wei, Liang Date: 2008/08/14. Outline. Introduction Alias Detection Method Data Representation Ranking Algorithms Experiment.

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Email Alias Detection Using Social Network Analysis 

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  1. Email Alias Detection Using Social Network Analysis  Ralf Holzer, Bradley Malin, Latanya Sweeney LinkKDD 2005 Advisor: Dr. Koh Jia-Ling Reporter: Che-Wei, Liang Date: 2008/08/14

  2. Outline • Introduction • Alias Detection Method • Data Representation • Ranking Algorithms • Experiment

  3. Introduction • Individuals use aliases for various communication purposes • Alias detection • Useful to both legitimate and illegitimate applications • Important to understand the extent to which the process can be automated

  4. Introduction • Aliases are listed on the same webpage can indicate there exists some form of relationship between them • Many people use several email addresses • This paper attempt to determine which email addresses correspond to the same entity

  5. Introduction • Email addresses, a type of alias, can be distilled from a large number of web pages • Such as class rosters, research papers, discussion boards • Email addresses provide a unique mapping from address to a specific entity

  6. Data Representation • Let S represent the set of sources Modeled as an undirected graph G = (I, E) • Ibe the set of unique email addresses • Cab = |eab| denote the number of sources associated with each edge connecting a and b

  7. Ranking Algorithms • Ranking method • Top-k list of possible aliases • Shortest path algorithm • Used geodesic distance to generate a ranking of nodes closest to a given originating node • Relationship strength is augmented with • Number of aliases on a source • Number of collocations of aliases

  8. Ranking Algorithms • Geodesic distance • Length of the shortest path from a to b • Potential aliases are ranked from lowest to highest geodesic distance • Multiple Collocation • Two aliases which collocate on more than one webpage signifies a stronger relationship

  9. Ranking Algorithms • Source Size • Strength between two aliases in inversely correlated with the number of aliases in a source • Combined • Integrates both of previous assumptions

  10. Experiment • Derived from CMU web pages • 1978 distinct email aliases • Data Set Statistics

  11. Experiment

  12. Experiment • Geodesic Alias Distances

  13. Experiment

  14. Experiment

  15. Experiment

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