Distributed Detection of Clusters of Arbitrary Size

Bogdan-Adrian Manghiuc

doi:10.1007/978-3-030-79527-6_21

Distributed Detection of Clusters of Arbitrary Size

Bogdan-Adrian Manghiuc

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

Abstract / Description of output

Graph clustering is a fundamental technique in data analysis with a vast number of applications in computer science and statistics. In theoretical computer science, the problem of graph clustering has received significant research attention over the past two decades, which has led to pivotal algorithmic breakthroughs. However, the design of most graph clustering algorithms is based on complicated techniques from computational optimisation, which are not applicable for processing massive data sets stored in physically remote locations.

Original language	English
Title of host publication	Structural Information and Communication Complexity
Subtitle of host publication	28th International Colloquium, SIROCCO 2021, Wrocław, Poland, June 28 – July 1, 2021, Proceedings
Editors	Tomasz Jurdziński, Stefan Schmid
Place of Publication	Cham
Publisher	Springer
Pages	370-387
Number of pages	18
ISBN (Electronic)	978-3-030-79527-6
ISBN (Print)	978-3-030-79526-9
DOIs	https://doi.org/10.1007/978-3-030-79527-6_21
Publication status	Published - 20 Jun 2021

Publication series

Name	Lecture Notes in Computer Science
Volume	12810
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords / Materials (for Non-textual outputs)

Distributed computing
Graph clustering
Randomised algorithms

Access to Document

10.1007/978-3-030-79527-6_21Licence: All Rights Reserved

https://link.springer.com/chapter/10.1007/978-3-030-79527-6_21Licence: All Rights Reserved

Cite this

Manghiuc, B.-A. (2021). Distributed Detection of Clusters of Arbitrary Size. In T. Jurdziński, & S. Schmid (Eds.), Structural Information and Communication Complexity: 28th International Colloquium, SIROCCO 2021, Wrocław, Poland, June 28 – July 1, 2021, Proceedings (pp. 370-387). (Lecture Notes in Computer Science; Vol. 12810). Springer. https://doi.org/10.1007/978-3-030-79527-6_21

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Manghiuc, B-A 2021, Distributed Detection of Clusters of Arbitrary Size. in T Jurdziński & S Schmid (eds), Structural Information and Communication Complexity: 28th International Colloquium, SIROCCO 2021, Wrocław, Poland, June 28 – July 1, 2021, Proceedings. Lecture Notes in Computer Science, vol. 12810, Springer, Cham, pp. 370-387. https://doi.org/10.1007/978-3-030-79527-6_21

Distributed Detection of Clusters of Arbitrary Size. / Manghiuc, Bogdan-Adrian.
Structural Information and Communication Complexity: 28th International Colloquium, SIROCCO 2021, Wrocław, Poland, June 28 – July 1, 2021, Proceedings. ed. / Tomasz Jurdziński; Stefan Schmid. Cham: Springer, 2021. p. 370-387 (Lecture Notes in Computer Science; Vol. 12810).

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

TY - GEN

T1 - Distributed Detection of Clusters of Arbitrary Size

AU - Manghiuc, Bogdan-Adrian

PY - 2021/6/20

Y1 - 2021/6/20

N2 - Graph clustering is a fundamental technique in data analysis with a vast number of applications in computer science and statistics. In theoretical computer science, the problem of graph clustering has received significant research attention over the past two decades, which has led to pivotal algorithmic breakthroughs. However, the design of most graph clustering algorithms is based on complicated techniques from computational optimisation, which are not applicable for processing massive data sets stored in physically remote locations.

AB - Graph clustering is a fundamental technique in data analysis with a vast number of applications in computer science and statistics. In theoretical computer science, the problem of graph clustering has received significant research attention over the past two decades, which has led to pivotal algorithmic breakthroughs. However, the design of most graph clustering algorithms is based on complicated techniques from computational optimisation, which are not applicable for processing massive data sets stored in physically remote locations.

KW - Distributed computing

KW - Graph clustering

KW - Randomised algorithms

U2 - 10.1007/978-3-030-79527-6_21

DO - 10.1007/978-3-030-79527-6_21

M3 - Conference contribution

SN - 978-3-030-79526-9

T3 - Lecture Notes in Computer Science

SP - 370

EP - 387

BT - Structural Information and Communication Complexity

A2 - Jurdziński, Tomasz

A2 - Schmid, Stefan

PB - Springer

CY - Cham