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چكيده انگليسي :

A Scalabe Method for One mode Projection of Bipartite Networks Mahsa Asadi Mahsa asadi@ec iut ac ir Date of Submission 09 06 2018 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiSupervisor Dr Nasser Ghadiri nghadiri@cc iut ac irAbstractHuman beings have been looking for models and methods to organize classify compress and filter theinformation due to the difficulty in maintenance and using immense sources of information Among all ofthe existed procedures for compressing information Recommender Systems can detect needed informationout of the huge amount of available information in a less time and propose it to the user through the help ofcomputational methods and artificial intelligence In addition presenting in graph format is one of the mostapplied models of rendering systems and their hidden information Bipartite graph has a specific application and importance among the variety of presenting methods since themajority of systems in this field such as recommender systems naturally model bipartite Most of bipartitenetworks tend to cluster one side of graph behavior in order to recognize communications and interactionsbetween members of that side and actually discover similar members One mode projection technique is anappropriate method for that purpose which recently has come to attention broadly in different areas such asSocial Networks Health Care systems medicine and treatment etc Generally some part of primaryinformation of main bipartite graph will be missed under the projection Hence scientists have been tryingto provide a method to determine the appropriate weight for yield projected edges in order to minimizeinformation loss On the other hand performance level on big data is an important challenge within these methods The majority of investigated databases in the field of bipartite network projection are huge consequently executing projection procedure takes lots of times Therefore we need methods which have acceptablespeed as they keep accuracy and precision in projection The following research aims to improve the existedalgorithm speed by introducing a scalable method based on resource allocation for bipartite networkprojection and we try to provide the appropriate speed while preserving precision through transferring theneeded operations on a distributed infrastructure like Hadoop Moreover as a case study we evaluate theperformance of presented scalable algorithm in the field of Social Network which results to a lesserprojection operation time in comparison to the undistributed mode Also we compared our proposedmethod with collaborative filtering method a well known algorithm in recommendation field and as a resultour method had higher execution speed overall With using the biggest dataset orkut the proposed methodhas higher speed than the scalable CF by 33 Then we evaluate the scalability of the introduced methodby a scalability metric named Speedup which showed good scalability Investigation accompanied withanalyzing the execution time increasing in different states and configurations based on input data sizegrowth KeywordsBipartite Networks Projection Recommendation Scalable

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