Quantum inspired evolutionary algorithm for community detection in complex networks
Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yuanyuan, Meng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Transient response and failure of medium density fibreboard panels subjected to air-blast loading - Langdon, G.S. ELSEVIER, 2021, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:382 ; year:2018 ; number:34 ; day:31 ; month:08 ; pages:2305-2312 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.physleta.2018.05.044 |
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| 520 | |a Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. | ||
| 520 | |a Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. | ||
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10.1016/j.physleta.2018.05.044 doi GBV00000000000297A.pica (DE-627)ELV043716342 (ELSEVIER)S0375-9601(18)30604-2 DE-627 ger DE-627 rakwb eng 530 530 DE-600 670 VZ 51.75 bkl Yuanyuan, Meng verfasserin aut Quantum inspired evolutionary algorithm for community detection in complex networks 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Quantum Elsevier Complex networks Elsevier Community detection Elsevier Xiyu, Liu oth Enthalten in North-Holland Publ Langdon, G.S. ELSEVIER Transient response and failure of medium density fibreboard panels subjected to air-blast loading 2021 Amsterdam (DE-627)ELV006407811 volume:382 year:2018 number:34 day:31 month:08 pages:2305-2312 extent:8 https://doi.org/10.1016/j.physleta.2018.05.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 382 2018 34 31 0831 2305-2312 8 045F 530 |
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10.1016/j.physleta.2018.05.044 doi GBV00000000000297A.pica (DE-627)ELV043716342 (ELSEVIER)S0375-9601(18)30604-2 DE-627 ger DE-627 rakwb eng 530 530 DE-600 670 VZ 51.75 bkl Yuanyuan, Meng verfasserin aut Quantum inspired evolutionary algorithm for community detection in complex networks 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Quantum Elsevier Complex networks Elsevier Community detection Elsevier Xiyu, Liu oth Enthalten in North-Holland Publ Langdon, G.S. ELSEVIER Transient response and failure of medium density fibreboard panels subjected to air-blast loading 2021 Amsterdam (DE-627)ELV006407811 volume:382 year:2018 number:34 day:31 month:08 pages:2305-2312 extent:8 https://doi.org/10.1016/j.physleta.2018.05.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 382 2018 34 31 0831 2305-2312 8 045F 530 |
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10.1016/j.physleta.2018.05.044 doi GBV00000000000297A.pica (DE-627)ELV043716342 (ELSEVIER)S0375-9601(18)30604-2 DE-627 ger DE-627 rakwb eng 530 530 DE-600 670 VZ 51.75 bkl Yuanyuan, Meng verfasserin aut Quantum inspired evolutionary algorithm for community detection in complex networks 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Quantum Elsevier Complex networks Elsevier Community detection Elsevier Xiyu, Liu oth Enthalten in North-Holland Publ Langdon, G.S. ELSEVIER Transient response and failure of medium density fibreboard panels subjected to air-blast loading 2021 Amsterdam (DE-627)ELV006407811 volume:382 year:2018 number:34 day:31 month:08 pages:2305-2312 extent:8 https://doi.org/10.1016/j.physleta.2018.05.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 382 2018 34 31 0831 2305-2312 8 045F 530 |
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10.1016/j.physleta.2018.05.044 doi GBV00000000000297A.pica (DE-627)ELV043716342 (ELSEVIER)S0375-9601(18)30604-2 DE-627 ger DE-627 rakwb eng 530 530 DE-600 670 VZ 51.75 bkl Yuanyuan, Meng verfasserin aut Quantum inspired evolutionary algorithm for community detection in complex networks 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. Quantum Elsevier Complex networks Elsevier Community detection Elsevier Xiyu, Liu oth Enthalten in North-Holland Publ Langdon, G.S. ELSEVIER Transient response and failure of medium density fibreboard panels subjected to air-blast loading 2021 Amsterdam (DE-627)ELV006407811 volume:382 year:2018 number:34 day:31 month:08 pages:2305-2312 extent:8 https://doi.org/10.1016/j.physleta.2018.05.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 382 2018 34 31 0831 2305-2312 8 045F 530 |
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Yuanyuan, Meng |
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quantum inspired evolutionary algorithm for community detection in complex networks |
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Quantum inspired evolutionary algorithm for community detection in complex networks |
| abstract |
Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. |
| abstractGer |
Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. |
| abstract_unstemmed |
Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. |
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Quantum inspired evolutionary algorithm for community detection in complex networks |
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https://doi.org/10.1016/j.physleta.2018.05.044 |
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