Email Alias Detection Using Social Network Analysis
This paper explores methods for detecting email aliases using social network analysis. It highlights the significance of aliases in communication and demonstrates how multiple email addresses can signify relationships between individuals. By representing data as an undirected graph and utilizing ranking algorithms, the study introduces a systematic approach to identify potential aliases based on geodesic distances and collocational patterns. The findings are based on an extensive dataset of distinct email aliases sourced from various web pages, revealing insights into automated alias detection processes.
Email Alias Detection Using Social Network Analysis
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Presentation Transcript
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
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
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
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
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
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
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
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
Experiment • Derived from CMU web pages • 1978 distinct email aliases • Data Set Statistics
Experiment • Geodesic Alias Distances
