NDLI: Parallelizing Preferential Attachment Models for Generating Large-Scale Social Networks that Cannot Fit into Memory

Content Provider	IEEE Xplore Digital Library
Author	Yi-Chen Lo Cheng-Te Li Shou-De Lin
Copyright Year	2012
Abstract	Social network generation is an important problem in social network analysis. The goal is to produce artificial networks that preserve some real world properties of social networks. As one of most popular social network generation algorithms, the Barabási -- Albert (BA) model is a method that can generate random social networks with power-law degree distribution. This paper discusses the situation of generating large-sized social network that cannot fit into the memory. We design a parallel framework to tackle this problem. The challenge lies in the fact that the preferential attachment mechanism used in the BA model has direct conflict with the concept of parallelism. To achieve the preferential attachment, during the generation processes the degree information of nodes needs to be known, which prohibits the parallelism that allows nodes to generate edges independently. To handle this issue, this paper proposes a method to generate the expected accumulated degree of vertices for the parallel BA model. We further propose several novel techniques to reduce the complexity of generating N vertices with P processes to O(NlogN/P). We implement the model using MapReduce and the experiment results show that our model can produce billion-sized scale-free networks in minutes.
Starting Page	229
Ending Page	238
File Size	476265
Page Count	10
File Format	PDF
ISBN	9781467356381
DOI	10.1109/SocialCom-PASSAT.2012.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-03
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Privacy Preferential attachment Conferences Social network services Large-scaled social networks BarabásiAlbert model Security MapReduce Graph mining Social network generation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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