Review of Parallel and Distributed Community Detection Algorithms

Raguru Jaya Krishna; Devi Prasad Sharma

doi:10.1007/978-981-15-9689-6_70

Review of Parallel and Distributed Community Detection Algorithms

Raguru Jaya Krishna^*, Devi Prasad Sharma

^*Corresponding author for this work

Department of Computer Science and Engineering, Manipal Institute of Technology, Bengaluru

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Community detection is an important part of the analysis of the social networks. In the present era of digital world, online communication, content sharing, and maintaining contacts are mostly happening through online social networks. These networks are ever increasing and they are becoming very complex to analyze information. The traditional techniques which are available for community detection in social networks are not able to process this large-scale social networks data in efficient manner. This brings an essential and efficient ways to find large social network communities. In this paper, we review different algorithms available in the literature to find communities in large-scale community detection. In addition, we also discuss about various types computing frameworks available to process community detection algorithms in parallel like GraphX, Map reduce.

Original language	English
Title of host publication	Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020
Editors	Dinesh Goyal, Amit Kumar Gupta, Vincenzo Piuri, Maria Ganzha, Marcin Paprzycki
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	639-644
Number of pages	6
ISBN (Print)	9789811596889
DOIs	https://doi.org/10.1007/978-981-15-9689-6_70
Publication status	Published - 2021
Event	2nd International Conference on Information Management and Machine Intelligence, ICIMMI 2020 - Jaipur, India Duration: 24-07-2020 → 25-07-2020

Publication series

Name	Lecture Notes in Networks and Systems
Volume	166
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Information Management and Machine Intelligence, ICIMMI 2020
Country/Territory	India
City	Jaipur
Period	24-07-20 → 25-07-20

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-15-9689-6_70

Cite this

Jaya Krishna, R., & Sharma, D. P. (2021). Review of Parallel and Distributed Community Detection Algorithms. In D. Goyal, A. K. Gupta, V. Piuri, M. Ganzha, & M. Paprzycki (Eds.), Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020 (pp. 639-644). (Lecture Notes in Networks and Systems; Vol. 166). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-9689-6_70

Jaya Krishna, Raguru ; Sharma, Devi Prasad. / Review of Parallel and Distributed Community Detection Algorithms. Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020. editor / Dinesh Goyal ; Amit Kumar Gupta ; Vincenzo Piuri ; Maria Ganzha ; Marcin Paprzycki. Springer Science and Business Media Deutschland GmbH, 2021. pp. 639-644 (Lecture Notes in Networks and Systems).

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abstract = "Community detection is an important part of the analysis of the social networks. In the present era of digital world, online communication, content sharing, and maintaining contacts are mostly happening through online social networks. These networks are ever increasing and they are becoming very complex to analyze information. The traditional techniques which are available for community detection in social networks are not able to process this large-scale social networks data in efficient manner. This brings an essential and efficient ways to find large social network communities. In this paper, we review different algorithms available in the literature to find communities in large-scale community detection. In addition, we also discuss about various types computing frameworks available to process community detection algorithms in parallel like GraphX, Map reduce.",

author = "{Jaya Krishna}, Raguru and Sharma, {Devi Prasad}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 2nd International Conference on Information Management and Machine Intelligence, ICIMMI 2020 ; Conference date: 24-07-2020 Through 25-07-2020",

year = "2021",

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isbn = "9789811596889",

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publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "639--644",

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booktitle = "Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020",

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Jaya Krishna, R & Sharma, DP 2021, Review of Parallel and Distributed Community Detection Algorithms. in D Goyal, AK Gupta, V Piuri, M Ganzha & M Paprzycki (eds), Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020. Lecture Notes in Networks and Systems, vol. 166, Springer Science and Business Media Deutschland GmbH, pp. 639-644, 2nd International Conference on Information Management and Machine Intelligence, ICIMMI 2020, Jaipur, India, 24-07-20. https://doi.org/10.1007/978-981-15-9689-6_70

Review of Parallel and Distributed Community Detection Algorithms. / Jaya Krishna, Raguru; Sharma, Devi Prasad.
Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020. ed. / Dinesh Goyal; Amit Kumar Gupta; Vincenzo Piuri; Maria Ganzha; Marcin Paprzycki. Springer Science and Business Media Deutschland GmbH, 2021. p. 639-644 (Lecture Notes in Networks and Systems; Vol. 166).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Review of Parallel and Distributed Community Detection Algorithms

AU - Jaya Krishna, Raguru

AU - Sharma, Devi Prasad

PY - 2021

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N2 - Community detection is an important part of the analysis of the social networks. In the present era of digital world, online communication, content sharing, and maintaining contacts are mostly happening through online social networks. These networks are ever increasing and they are becoming very complex to analyze information. The traditional techniques which are available for community detection in social networks are not able to process this large-scale social networks data in efficient manner. This brings an essential and efficient ways to find large social network communities. In this paper, we review different algorithms available in the literature to find communities in large-scale community detection. In addition, we also discuss about various types computing frameworks available to process community detection algorithms in parallel like GraphX, Map reduce.

AB - Community detection is an important part of the analysis of the social networks. In the present era of digital world, online communication, content sharing, and maintaining contacts are mostly happening through online social networks. These networks are ever increasing and they are becoming very complex to analyze information. The traditional techniques which are available for community detection in social networks are not able to process this large-scale social networks data in efficient manner. This brings an essential and efficient ways to find large social network communities. In this paper, we review different algorithms available in the literature to find communities in large-scale community detection. In addition, we also discuss about various types computing frameworks available to process community detection algorithms in parallel like GraphX, Map reduce.

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T3 - Lecture Notes in Networks and Systems

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BT - Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020

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A2 - Ganzha, Maria

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PB - Springer Science and Business Media Deutschland GmbH

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Jaya Krishna R, Sharma DP. Review of Parallel and Distributed Community Detection Algorithms. In Goyal D, Gupta AK, Piuri V, Ganzha M, Paprzycki M, editors, Proceedings of the Second International Conference on Information Management and Machine Intelligence, ICIMMI 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 639-644. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-15-9689-6_70

Review of Parallel and Distributed Community Detection Algorithms

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