[Colloq] [Lazer+MoBS Lab] Talk: Measuring Tie Strength in Implicit Social Networks

[Nicole Bekerian]

Dear all,

Prof. Tina Eliassi-Rad, whose research focuses on analyzing large complex relational data, will be coming on Wed. Dec. 7. She will be giving a talk at 3pm at CCNR. Please see the talk abstract and her bio below. Her website: http://eliassi.org/

I’m coordinating her meetings. If you would like to meet with her before or after her talk, please let me know ASAP.

cheers,
yuru

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Title: Measuring Tie Strength in Implicit Social Networks

Abstract: Given a set of people and a set of events they attend, we address the problem of measuring connectedness or tie strength between each pair of persons given that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to this problem. Starting from a list of axioms that a measure of tie strength must satisfy, we characterize functions that satisfy all the axioms and show that there is a range of measures that satisfy this characterization. A measure of tie strength induces a ranking on the edges (and on the set of neighbors for every person). We show that for applications where the ranking, and not the absolute value of the tie strength, is the important thing about the measure, the axioms are equivalent to a natural partial order. Also, to settle on a particular measure, we must make a non-obvious decision about extending this partial order to a total order, and that this decision is best left to particular applications. We classify measures found in prior literature according to the axioms that they satisfy. In our experiments, we measure tie strength and the coverage of our axioms in several datasets. Also, for each dataset, we bound the maximum Kendall’s Tau divergence (which measures the number of pairwise disagreements between two lists) between all measures that satisfy the axioms using the partial order. This informs us if particular datasets are well behaved where we do not have to worry about which measure to choose, or we have to be careful about the exact choice of measure we make.

Bio: Tina Eliassi-Rad is an Assistant Professor of Computer Science at Rutgers University. She earned her Ph.D. in Computer Sciences at the University of Wisconsin-Madison. Prior to joining Rutgers, Tina was a Member of Technical Staff at Lawrence Livermore National Laboratory.
Broadly speaking, her research interests include data mining, machine learning, and artificial intelligence. Tina's work has been applied to the World-Wide Web, large-scale scientific simulation data, complex networks, and cyber situational awareness. Tina is an action editor for the Data Mining and Knowledge Discovery Journal. In 2010, she received an Outstanding Mentor Award from the US DOE Office of Science.


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Best, 
Nicole 

______________________________________________________________ 

Nicole Bekerian 
Administrative Assistant 

Northeastern University 
College of Computer and Information Science 
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202 West Village H 
Boston, MA 02115 

Phone: 617.373.2462 
Fax: 617.373.5121