Community Detection Based on Minimum-Cut Graph Partitioning

Community Detection Using Graph Partitioning

10.1109/gcat52182.2021.9587685 ◽

2021 ◽

Author(s):

R C Jisha ◽

P S Indrajith ◽

S Abhishek

Keyword(s):

Community Detection ◽

Graph Partitioning

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Community Structure Detection Using Firefly Algorithm

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2018100103 ◽

2018 ◽

Vol 9 (4) ◽

pp. 52-70 ◽

Cited By ~ 5

Author(s):

Ameera Saleh Jaradat ◽

Safa'a Bani Hamad

Keyword(s):

Community Structure ◽

Community Detection ◽

Graph Partitioning ◽

Firefly Algorithm ◽

Real Life ◽

People Detection ◽

Continuous Growth ◽

Structure Detection ◽

Partitioning Problem ◽

And Function

This article describes how parallel to the continuous growth of the Internet, which allows people to share and collaborate more, social networks have become more attractive as a research topic in many different disciplines. Community structures are established upon interactions between people. Detection of these communities has become a popular topic in computer science. How to detect the communities is of great importance for understanding the organization and function of networks. Community detection is considered a variant of the graph partitioning problem which is NP-hard. In this article, the Firefly algorithm is used as an optimization algorithm to solve the community detection problem by maximizing the modularity measure. Firefly algorithm is a new Nature-inspired heuristic algorithm that proved its good performance in a variety of applications. Experimental results obtained from tests on real-life networks demonstrate that the authors' algorithm successfully detects the community structure.

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Spectral methods for community detection and graph partitioning

Physical Review E ◽

10.1103/physreve.88.042822 ◽

2013 ◽

Vol 88 (4) ◽

Cited By ~ 171

Author(s):

M. E. J. Newman

Keyword(s):

Community Detection ◽

Spectral Methods ◽

Graph Partitioning

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Partitioning of Complex Networks for Heterogeneous Computing

Advances in Computer and Electrical Engineering - Creativity in Load-Balance Schemes for Multi/Many-Core Heterogeneous Graph Computing ◽

10.4018/978-1-5225-3799-1.ch004 ◽

2018 ◽

pp. 88-112

Keyword(s):

Community Detection ◽

Graph Partitioning ◽

Heterogeneous Computing ◽

Fundamental Problem ◽

Vital Role ◽

Graph Processing ◽

Graph Partitions ◽

Hardware Architectures ◽

Graph Computing ◽

Parallel Graph

The most fundamental problem in BSP parallel graph computing is to decide how to partition and then distribute the graph among the available processors. In this regard, partitioning techniques for BSP heterogeneous computing should produce computing loads with different sizes (unbalanced partitions) in order to exploit processors with different computing capabilities. In this chapter, three major graph partitioning paradigms that are relevant to parallel graph processing are reviewed: balanced graph partitioning, unbalanced graph partitioning, and community detection. Then, the authors discuss how any of these paradigms fits the needs of graph heterogeneous computing where the suitability of partitions to hardware architectures plays a vital role. Finally, the authors discuss how the decomposition of networks in layers through the k-core decomposition provides the means for developing methods to produce unbalanced graph partitions that match multi-core and GPU processing capabilities.

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Towards a parallel system for demographic zonification based on complex networks

Journal of Applied Research and Technology ◽

10.22201/icat.16656423.2009.7.02.519 ◽

2009 ◽

Vol 7 (02) ◽

Cited By ~ 4

Author(s):

Alberto Ochoa ◽

Beatríz Bernábe ◽

Omar Ochoa

Keyword(s):

Complex Networks ◽

Community Detection ◽

Graph Partitioning ◽

Betweenness Centrality ◽

Population Data ◽

Detection Algorithm ◽

Computing Platform ◽

Parallel Version ◽

Novel Method ◽

Community Detection Algorithm

This paper presents a novel method for the zone design problem that is based on a popular technique in the field of complex networks research: betweenness centrality. We use a parallel version of a community detection algorithm that outputs the graph partitioning that maximizes the so‐called modularity metric. The method is put at the centre of an effort to build an open source interactive high erformance computing platform to assist researchers working with population data.

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