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Title: A Parallel Algorithm for Community Detection in Social Networks, Based on Path Analysis and Threaded Binary Trees
Authors: Souravlas, Stavros
Sifaleras, Angelo
Katsavounis, Stefanos
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Natural sciences::Mathematics::Applied Mathematics
Keywords: Community detection
Parallel algorithms
Binary trees
Social circles
Issue Date: 2019
Publisher: IEEE
Source: IEEE Access
Volume: 7
First Page: 20499
Last Page: 20519
Abstract: Several synchronous applications are based on the graph-structured data; among them, a very important application of this kind is community detection. Since the number and size of the networks modeled by graphs grow larger and larger, some level of parallelism needs to be used, to reduce the computational costs of such massive applications. Social networking sites allow users to manually categorize their friends into social circles (referred to as lists on Facebook and Twitter), while users, based on their interests, place themselves into groups of interest. However, the community detection and is a very effortful procedure, and in addition, these communities need to be updated very often, resulting in more effort. In this paper, we combine parallel processing techniques with a typical data structure like threaded binary trees to detect communities in an efficient manner. Our strategy is implemented over weighted networks with irregular topologies and it is based on a stepwise path detection strategy, where each step finds a link that increases the overall strength of the path being detected. To verify the functionality and parallelism benefits of our scheme, we perform experiments on five real-world data sets: Facebook ® , Twitter ® , Google+ ® , Pokec, and LiveJournal.
ISSN: 2169-3536
Other Identifiers: 10.1109/ACCESS.2019.2897783
Appears in Collections:Department of Applied Informatics

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