Models & Optimisation and Mathematical Analysis Journal
Volume 7, Numéro 1, Pages 16-20

Measurement And Social Network Analysis With Parallel Frequent Pattern Mining

Authors : Yermes Mohammed El Amine . Rebbah Mohammed .

Abstract

Social network analysis (‘SNA’) measures are a vital tool for understanding the behavior of networks and graphs. However, the huge amount of data generated by networks require new techniques to optimize the calculation. Many efficient algorithms based on graph calculation have been used in the last decades; most of them do not scale the large amount of data. In this paper, we propose a new approach based on parallel frequent pattern mining as an essential data-mining task, with a goal of calculating degree centralities. To take advantage of the computing power provided by HPCs, the use of a hybrid distribution of data (horizontal and vertical) seems necessary to reduce the computing time. The implementation show how is benefic to combine parallel and distributed programming techniques such as MPI with data mining tools.

Keywords

Parallel Frequent Pattern Mining ; Social Network Analysis ; Degree Centralities ; HPC