Tehran Institute for Advanced Studies (TeIAS)

/ Scalable Betweenness Centrality Maximization via Sampling __ Ahmad Mahmoody


Scalable Betweenness Centrality Maximization via Sampling

August 29, 2018


Khatam University, Building No2.
Address: Mollasadra Blvd., North Shirazi St., East Daneshvar St., No.17. See location on Google map


Dr. Ahmad Mahmoody

Applied Scientist at Microsoft


Sampling based and randomized algorithms are powerful means in studying big data problems, In this talk we introduce some tools in statistical learning theory and show how they can be applied to big data problems. Using these tools, we study the “centrality” problem in the context of social network analysis, and we provide the state-of-the-art algorithm with theoretical guarantees.


Ahmad Mahmoody based is an Applied Scientist at Microsoft. He received his Phd in Computer Science from Brown University, mainly focused on problems in Big Data/Graphs mining. He finished his Undergraduate and Master studies in mathematics from Sharif University of Technology and Simon Fraser University.