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
Gespeichert in:
| Autor*in: |
Cai, Qing [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
10 |
|---|
| 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 |
|---|---|
| Übergeordnetes Werk: |
volume:58 ; year:2014 ; pages:4-13 ; extent:10 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neunet.2014.04.006 |
|---|
| Katalog-ID: |
ELV034009353 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV034009353 | ||
| 003 | DE-627 | ||
| 005 | 20230625195619.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2014 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.neunet.2014.04.006 |2 doi | |
| 028 | 5 | 2 | |a GBVA2014015000004.pica |
| 035 | |a (DE-627)ELV034009353 | ||
| 035 | |a (ELSEVIER)S0893-6080(14)00090-2 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 004 | |
| 082 | 0 | 4 | |a 004 |q DE-600 |
| 082 | 0 | 4 | |a 620 |q VZ |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 77.50 |2 bkl | ||
| 100 | 1 | |a Cai, Qing |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Discrete particle swarm optimization for identifying community structures in signed social networks |
| 264 | 1 | |c 2014transfer abstract | |
| 300 | |a 10 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 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. | ||
| 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. | ||
| 650 | 7 | |a Signed social network |2 Elsevier | |
| 650 | 7 | |a Evolutionary algorithm |2 Elsevier | |
| 650 | 7 | |a Community detection |2 Elsevier | |
| 650 | 7 | |a Particle swarm optimization |2 Elsevier | |
| 700 | 1 | |a Gong, Maoguo |4 oth | |
| 700 | 1 | |a Shen, Bo |4 oth | |
| 700 | 1 | |a Ma, Lijia |4 oth | |
| 700 | 1 | |a Jiao, Licheng |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |t Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |d 2012 |d the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society |g Amsterdam |w (DE-627)ELV016218965 |
| 773 | 1 | 8 | |g volume:58 |g year:2014 |g pages:4-13 |g extent:10 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.neunet.2014.04.006 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 77.50 |j Psychophysiologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 58 |j 2014 |h 4-13 |g 10 | ||
| 953 | |2 045F |a 004 | ||
| author_variant |
q c qc |
|---|---|
| matchkey_str |
caiqinggongmaoguoshenbomalijiajiaolichen:2014----:iceeatcewrotmztofrdniyncmuiytutrs |
| hierarchy_sort_str |
2014transfer abstract |
| bklnumber |
77.50 |
| publishDate |
2014 |
| allfields |
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 |
| allfields_unstemmed |
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 |
| language |
English |
| source |
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 |
| sourceStr |
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 |
| format_phy_str_mv |
Article |
| bklname |
Psychophysiologie |
| institution |
findex.gbv.de |
| topic_facet |
Signed social network Evolutionary algorithm Community detection Particle swarm optimization |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
| authorswithroles_txt_mv |
Cai, Qing @@aut@@ Gong, Maoguo @@oth@@ Shen, Bo @@oth@@ Ma, Lijia @@oth@@ Jiao, Licheng @@oth@@ |
| publishDateDaySort_date |
2014-01-01T00:00:00Z |
| hierarchy_top_id |
ELV016218965 |
| dewey-sort |
14 |
| id |
ELV034009353 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV034009353</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625195619.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neunet.2014.04.006</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014015000004.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV034009353</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0893-6080(14)00090-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">77.50</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cai, Qing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Discrete particle swarm optimization for identifying community structures in signed social networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">10</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Signed social network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Evolutionary algorithm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Community detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Particle swarm optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gong, Maoguo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shen, Bo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Lijia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiao, Licheng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="t">Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing</subfield><subfield code="d">2012</subfield><subfield code="d">the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV016218965</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:58</subfield><subfield code="g">year:2014</subfield><subfield code="g">pages:4-13</subfield><subfield code="g">extent:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neunet.2014.04.006</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">77.50</subfield><subfield code="j">Psychophysiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">58</subfield><subfield code="j">2014</subfield><subfield code="h">4-13</subfield><subfield code="g">10</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| author |
Cai, Qing |
| spellingShingle |
Cai, Qing ddc 004 ddc 620 ddc 610 bkl 77.50 Elsevier Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization Discrete particle swarm optimization for identifying community structures in signed social networks |
| authorStr |
Cai, Qing |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV016218965 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science 620 - Engineering & allied operations 610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
004 004 DE-600 620 VZ 610 VZ 77.50 bkl Discrete particle swarm optimization for identifying community structures in signed social networks Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization Elsevier |
| topic |
ddc 004 ddc 620 ddc 610 bkl 77.50 Elsevier Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization |
| topic_unstemmed |
ddc 004 ddc 620 ddc 610 bkl 77.50 Elsevier Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization |
| topic_browse |
ddc 004 ddc 620 ddc 610 bkl 77.50 Elsevier Signed social network Elsevier Evolutionary algorithm Elsevier Community detection Elsevier Particle swarm optimization |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
m g mg b s bs l m lm l j lj |
| hierarchy_parent_title |
Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
| hierarchy_parent_id |
ELV016218965 |
| dewey-tens |
000 - Computer science, knowledge & systems 620 - Engineering 610 - Medicine & health |
| hierarchy_top_title |
Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV016218965 |
| title |
Discrete particle swarm optimization for identifying community structures in signed social networks |
| ctrlnum |
(DE-627)ELV034009353 (ELSEVIER)S0893-6080(14)00090-2 |
| title_full |
Discrete particle swarm optimization for identifying community structures in signed social networks |
| author_sort |
Cai, Qing |
| journal |
Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
| journalStr |
Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2014 |
| contenttype_str_mv |
zzz |
| container_start_page |
4 |
| author_browse |
Cai, Qing |
| container_volume |
58 |
| physical |
10 |
| class |
004 004 DE-600 620 VZ 610 VZ 77.50 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Cai, Qing |
| doi_str_mv |
10.1016/j.neunet.2014.04.006 |
| dewey-full |
004 620 610 |
| title_sort |
discrete particle swarm optimization for identifying community structures in signed social networks |
| title_auth |
Discrete particle swarm optimization for identifying community structures in signed social networks |
| abstract |
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. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Discrete particle swarm optimization for identifying community structures in signed social networks |
| url |
https://doi.org/10.1016/j.neunet.2014.04.006 |
| remote_bool |
true |
| author2 |
Gong, Maoguo Shen, Bo Ma, Lijia Jiao, Licheng |
| author2Str |
Gong, Maoguo Shen, Bo Ma, Lijia Jiao, Licheng |
| ppnlink |
ELV016218965 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1016/j.neunet.2014.04.006 |
| up_date |
2024-07-06T20:02:13.622Z |
| _version_ |
1803861238610919424 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV034009353</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625195619.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neunet.2014.04.006</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014015000004.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV034009353</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0893-6080(14)00090-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">77.50</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cai, Qing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Discrete particle swarm optimization for identifying community structures in signed social networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">10</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Signed social network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Evolutionary algorithm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Community detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Particle swarm optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gong, Maoguo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shen, Bo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Lijia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiao, Licheng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="t">Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing</subfield><subfield code="d">2012</subfield><subfield code="d">the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV016218965</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:58</subfield><subfield code="g">year:2014</subfield><subfield code="g">pages:4-13</subfield><subfield code="g">extent:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neunet.2014.04.006</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">77.50</subfield><subfield code="j">Psychophysiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">58</subfield><subfield code="j">2014</subfield><subfield code="h">4-13</subfield><subfield code="g">10</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| score |
7.3977823 |