CATS: A big network clustering algorithm based on triangle structures

Mincheol Shin; Jeongwoo Kim; Jungrim Kim; Dongmin Seo; Chihyun Park; Seok Jong Yu; Sanghyun Park

doi:10.1145/3019612.3019893

CATS: A big network clustering algorithm based on triangle structures

Mincheol Shin, Jeongwoo Kim, Jungrim Kim, Dongmin Seo, Chihyun Park, Seok Jong Yu, Sanghyun Park

Department of Computer Science

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

1 Citation (Scopus)

Abstract

A huge amount of data, known as "big data," has been generated from various areas. A network is a popular data structure for presenting and analyzing big data. However, the conventional network analysis algorithms cannot cover the size of big data. To address this limitation, we propose in this paper a network clustering algorithm for a big data network using a parallel distributed computation model. To consider parallel computation concepts, we change the paradigm of the conventional clustering algorithm using triangle structures. We demonstrate that the proposed algorithm can cover a big data network that cannot be otherwise implemented using a conventional algorithm. Experimental results show that the proposed algorithm is faster than the conventional algorithm.

Original language	English
Title of host publication	32nd Annual ACM Symposium on Applied Computing, SAC 2017
Publisher	Association for Computing Machinery
Pages	1590-1592
Number of pages	3
ISBN (Electronic)	9781450344869
DOIs	https://doi.org/10.1145/3019612.3019893
Publication status	Published - 2017 Apr 3
Event	32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco Duration: 2017 Apr 4 → 2017 Apr 6

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing
Volume	Part F128005

Other

Other	32nd Annual ACM Symposium on Applied Computing, SAC 2017
Country	Morocco
City	Marrakesh
Period	17/4/4 → 17/4/6

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All Science Journal Classification (ASJC) codes

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Cite this

Shin, M., Kim, J., Kim, J., Seo, D., Park, C., Yu, S. J., & Park, S. (2017). CATS: A big network clustering algorithm based on triangle structures. In 32nd Annual ACM Symposium on Applied Computing, SAC 2017 (pp. 1590-1592). (Proceedings of the ACM Symposium on Applied Computing; Vol. Part F128005). Association for Computing Machinery. https://doi.org/10.1145/3019612.3019893

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title = "CATS: A big network clustering algorithm based on triangle structures",

abstract = "A huge amount of data, known as {"}big data,{"} has been generated from various areas. A network is a popular data structure for presenting and analyzing big data. However, the conventional network analysis algorithms cannot cover the size of big data. To address this limitation, we propose in this paper a network clustering algorithm for a big data network using a parallel distributed computation model. To consider parallel computation concepts, we change the paradigm of the conventional clustering algorithm using triangle structures. We demonstrate that the proposed algorithm can cover a big data network that cannot be otherwise implemented using a conventional algorithm. Experimental results show that the proposed algorithm is faster than the conventional algorithm.",

author = "Mincheol Shin and Jeongwoo Kim and Jungrim Kim and Dongmin Seo and Chihyun Park and Yu, {Seok Jong} and Sanghyun Park",

year = "2017",

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doi = "10.1145/3019612.3019893",

language = "English",

series = "Proceedings of the ACM Symposium on Applied Computing",

publisher = "Association for Computing Machinery",

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booktitle = "32nd Annual ACM Symposium on Applied Computing, SAC 2017",

note = "32nd Annual ACM Symposium on Applied Computing, SAC 2017 ; Conference date: 04-04-2017 Through 06-04-2017",

}

Shin, M, Kim, J, Kim, J, Seo, D, Park, C, Yu, SJ & Park, S 2017, CATS: A big network clustering algorithm based on triangle structures. in 32nd Annual ACM Symposium on Applied Computing, SAC 2017. Proceedings of the ACM Symposium on Applied Computing, vol. Part F128005, Association for Computing Machinery, pp. 1590-1592, 32nd Annual ACM Symposium on Applied Computing, SAC 2017, Marrakesh, Morocco, 17/4/4. https://doi.org/10.1145/3019612.3019893

CATS : A big network clustering algorithm based on triangle structures. / Shin, Mincheol; Kim, Jeongwoo; Kim, Jungrim; Seo, Dongmin; Park, Chihyun; Yu, Seok Jong; Park, Sanghyun.

32nd Annual ACM Symposium on Applied Computing, SAC 2017. Association for Computing Machinery, 2017. p. 1590-1592 (Proceedings of the ACM Symposium on Applied Computing; Vol. Part F128005).

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

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AB - A huge amount of data, known as "big data," has been generated from various areas. A network is a popular data structure for presenting and analyzing big data. However, the conventional network analysis algorithms cannot cover the size of big data. To address this limitation, we propose in this paper a network clustering algorithm for a big data network using a parallel distributed computation model. To consider parallel computation concepts, we change the paradigm of the conventional clustering algorithm using triangle structures. We demonstrate that the proposed algorithm can cover a big data network that cannot be otherwise implemented using a conventional algorithm. Experimental results show that the proposed algorithm is faster than the conventional algorithm.

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Access to Document

10.1145/3019612.3019893