A Fast Method of Detecting Overlapping Community in Network Based on LFM

Li, Yanan; Zhu, Zhengyu

doi:10.17706/jsw.10.7.825-834

Cited by 3 publications

(2 citation statements)

References 20 publications

(21 reference statements)

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“…Given a seed community, if all other vertexes can try to join to it, but not only those not joined to any community yet, this strategy can be used for the detection of overlapping communities. The algorithms IS(Iterative Scan) [28], RaRe(Rank Removal) [28], IS 2 (Improved IS) [29], LFM [30], fast LFM [31], DOCS(Detecting Overlapping Community Structures) [32], MOSES(Model-based Overlapping Seed Expan-Sion) [33], and OSLOM(Order Statistics Local Optimization Method) [34] are just some examples. However, the results of such an algorithm heavily depends on the quality of the selected seeds.…”

Section: Local Expansionmentioning

confidence: 99%

A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks

Sun

Jie

Sauer

et al. 2018

Future Generation Computer Systems

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Community detection is a critical task for complex network analysis. It helps us to understand the properties of the system that a complex network represents and has significance to a wide range of applications. Though a large number of algorithms have been developed, the detection of overlapping communities from large scale and (or) dynamic networks still remains challenging. In this paper, a Parallel Self-organizing Overlapping Community Detection (PSOCD) algorithm ground on the idea of swarm intelligence is proposed. The PSOCD is designed based on the concept of swarm intelligence system where an analyzed network is treated as a decentralized, self-organized, and self-evolving systems, in which each vertex acts iteratively to join to or leave from communities based on a set of predefined simple vertex action rules. The algorithm is implemented on a distributed graph processing platform named Giraph++; therefore it is capable of analyzing large scale networks.

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Section: Local Expansionmentioning

confidence: 99%

A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks

Sun

Jie

Sauer

et al. 2018

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Given a seed community, if all other vertexes can try to join into it, but not only those not joined into any community yet, this strategy can be used for the detection of overlapping communities. The algorithms IS(Iterative Scan) [15], RaRe(Rank Removal) [15], IS 2 (Improved IS) [16], LFM [17], fast LFM [18], DOCS(Detecting Overlapping Community Structures) [19], MOSES(Model-based Overlapping Seed ExpanSion) [20], and OSLOM(Order Statistics Local Optimization Method) [21] are just some examples. However, the results of such an algorithm heavily depends on the quality of the selected seeds.…”

Section: B Local Expansionmentioning

confidence: 99%

A Parallel Self-Organizing Community Detection Algorithm Based on Swarm Intelligence for Large Scale Complex Networks

Sun¹,

Jie²,

Sauer³

et al. 2017

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)

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Abstract-Community detection is a critical task for complex network analysis. It helps us to understand the properties of the system that a complex network represents and has significance to a wide range of applications. Nowadays, the challenges faced by community detection algorithms include overlapping community structure detection, large scale network analysis, dynamic changing of analyzed network topology and many more. In this paper a self-organizing community detection algorithm, based on the idea of swarm intelligence, was proposed and its parallel algorithm was designed on Giraph++ which is a semiasynchronous parallel graph computation framework running on distributed environment. In the algorithm, a network of large size is firstly divided into a number of small sub-networks. Then, each sub-network is modeled as a self-evolving swarm intelligence sub-system, while each vertex within the sub-network acts iteratively to join into or leave from communities based on a set of predefined vertex action rules. Meanwhile, the local communities of a sub-network are sent to other sub-networks to make their members have a chance to join into, therefore connecting these self-evolving swarm intelligence sub-systems together as a whole, large and evolving, system. The vertex actions during evolution of a sub-network are sent as well to keep multiple community replicas being consistent. Thus network communication efficiency has a great impact on the algorithm' performance. While there is no vertex changing in its belonging communities anymore, an optimal community structure of the whole network will have emerged as a result. In the algorithm it is natural that a vertex can join into multiple communities simultaneously, thus can be used for overlapping community detection. The algorithm deals with vertex and edge adding or deleting in the same way as the algorithm running, therefore inherently supports dynamic network analysis. The algorithm can be used for the analysis of large scale networks with its parallel version running on distributed environment. A variety of experiments conducted on synthesized networks have shown that the proposed algorithm can effectively detect community structures and its performance is much better than certain popular community detection algorithms.

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An Improved LFM Algorithm Based on Fitness Function and Community Similarity

Zhang

Yan

2019

2019 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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A Fast Method of Detecting Overlapping Community in Network Based on LFM

Cited by 3 publications

References 20 publications

A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks

A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks

A Parallel Self-Organizing Community Detection Algorithm Based on Swarm Intelligence for Large Scale Complex Networks

An Improved LFM Algorithm Based on Fitness Function and Community Similarity

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