Discrete particle swarm optimization for identifying community structures in signed social networks
Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be...
Ausführliche Beschreibung
Autor*in: |
Cai, Qing [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2014transfer abstract |
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Schlagwörter: |
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Umfang: |
10 |
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Übergeordnetes Werk: |
Enthalten in: Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing - 2012, the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, Amsterdam |
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Übergeordnetes Werk: |
volume:58 ; year:2014 ; pages:4-13 ; extent:10 |
Links: |
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DOI / URN: |
10.1016/j.neunet.2014.04.006 |
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Katalog-ID: |
ELV034009353 |
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520 | |a Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. | ||
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10.1016/j.neunet.2014.04.006 doi GBVA2014015000004.pica (DE-627)ELV034009353 (ELSEVIER)S0893-6080(14)00090-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Cai, Qing verfasserin aut Discrete particle swarm optimization for identifying community structures in signed social networks 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization Elsevier Gong, Maoguo oth Shen, Bo oth Ma, Lijia oth Jiao, Licheng oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:58 year:2014 pages:4-13 extent:10 https://doi.org/10.1016/j.neunet.2014.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 58 2014 4-13 10 045F 004 |
spelling |
10.1016/j.neunet.2014.04.006 doi GBVA2014015000004.pica (DE-627)ELV034009353 (ELSEVIER)S0893-6080(14)00090-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Cai, Qing verfasserin aut Discrete particle swarm optimization for identifying community structures in signed social networks 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization Elsevier Gong, Maoguo oth Shen, Bo oth Ma, Lijia oth Jiao, Licheng oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:58 year:2014 pages:4-13 extent:10 https://doi.org/10.1016/j.neunet.2014.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 58 2014 4-13 10 045F 004 |
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10.1016/j.neunet.2014.04.006 doi GBVA2014015000004.pica (DE-627)ELV034009353 (ELSEVIER)S0893-6080(14)00090-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Cai, Qing verfasserin aut Discrete particle swarm optimization for identifying community structures in signed social networks 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization Elsevier Gong, Maoguo oth Shen, Bo oth Ma, Lijia oth Jiao, Licheng oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:58 year:2014 pages:4-13 extent:10 https://doi.org/10.1016/j.neunet.2014.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 58 2014 4-13 10 045F 004 |
allfieldsGer |
10.1016/j.neunet.2014.04.006 doi GBVA2014015000004.pica (DE-627)ELV034009353 (ELSEVIER)S0893-6080(14)00090-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Cai, Qing verfasserin aut Discrete particle swarm optimization for identifying community structures in signed social networks 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization Elsevier Gong, Maoguo oth Shen, Bo oth Ma, Lijia oth Jiao, Licheng oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:58 year:2014 pages:4-13 extent:10 https://doi.org/10.1016/j.neunet.2014.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 58 2014 4-13 10 045F 004 |
allfieldsSound |
10.1016/j.neunet.2014.04.006 doi GBVA2014015000004.pica (DE-627)ELV034009353 (ELSEVIER)S0893-6080(14)00090-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Cai, Qing verfasserin aut Discrete particle swarm optimization for identifying community structures in signed social networks 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization Elsevier Gong, Maoguo oth Shen, Bo oth Ma, Lijia oth Jiao, Licheng oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:58 year:2014 pages:4-13 extent:10 https://doi.org/10.1016/j.neunet.2014.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 58 2014 4-13 10 045F 004 |
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Enthalten in Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing Amsterdam volume:58 year:2014 pages:4-13 extent:10 |
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Enthalten in Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing Amsterdam volume:58 year:2014 pages:4-13 extent:10 |
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Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
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Cai, Qing @@aut@@ Gong, Maoguo @@oth@@ Shen, Bo @@oth@@ Ma, Lijia @@oth@@ Jiao, Licheng @@oth@@ |
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Discrete particle swarm optimization for identifying community structures in signed social networks |
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Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. |
abstractGer |
Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. |
abstract_unstemmed |
Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles’ status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles’ updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. |
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Discrete particle swarm optimization for identifying community structures in signed social networks |
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