David F. Gleich
homepage:http://www.cs.purdue.edu/homes/dgleich/
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Description

Professor Gleich is interested in how we can utilize matrix algebra to express -- and improve -- algorithms in network analysis and data-based simulation analysis. Matrix algebra is a particularly attractive paradigm to study these procedures as it often gives rise to efficient computational procedures in a variety of settings (serial, parallel, streaming). This research straddles a few different areas and often involves working with large datasets on high performance computing architectures (e.g. MPI clusters) and data computing architectures (e.g. MapReduce).


Lecture:

invited talk
flag Personalized PageRank based Community Detection
as author at  The 11th Workshop on Mining and Learning with Graphs (MLG) 2013,
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