ACO-IM: maximizing influence in social networks using ant colony optimization
Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a smal...
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| Autor*in: |
Singh, Shashank Sheshar [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 24(2019), 13 vom: 28. Nov., Seite 10181-10203 |
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| Übergeordnetes Werk: |
volume:24 ; year:2019 ; number:13 ; day:28 ; month:11 ; pages:10181-10203 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-019-04533-y |
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OLC2034909623 |
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| 520 | |a Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. | ||
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10.1007/s00500-019-04533-y doi (DE-627)OLC2034909623 (DE-He213)s00500-019-04533-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Singh, Shashank Sheshar verfasserin (orcid)0000-0003-0909-2258 aut ACO-IM: maximizing influence in social networks using ant colony optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. Information diffusion Influence maximization Social networks Ant colony optimization Singh, Kuldeep aut Kumar, Ajay aut Biswas, Bhaskar aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 24(2019), 13 vom: 28. Nov., Seite 10181-10203 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:24 year:2019 number:13 day:28 month:11 pages:10181-10203 https://doi.org/10.1007/s00500-019-04533-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 24 2019 13 28 11 10181-10203 |
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10.1007/s00500-019-04533-y doi (DE-627)OLC2034909623 (DE-He213)s00500-019-04533-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Singh, Shashank Sheshar verfasserin (orcid)0000-0003-0909-2258 aut ACO-IM: maximizing influence in social networks using ant colony optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. Information diffusion Influence maximization Social networks Ant colony optimization Singh, Kuldeep aut Kumar, Ajay aut Biswas, Bhaskar aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 24(2019), 13 vom: 28. Nov., Seite 10181-10203 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:24 year:2019 number:13 day:28 month:11 pages:10181-10203 https://doi.org/10.1007/s00500-019-04533-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 24 2019 13 28 11 10181-10203 |
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10.1007/s00500-019-04533-y doi (DE-627)OLC2034909623 (DE-He213)s00500-019-04533-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Singh, Shashank Sheshar verfasserin (orcid)0000-0003-0909-2258 aut ACO-IM: maximizing influence in social networks using ant colony optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. Information diffusion Influence maximization Social networks Ant colony optimization Singh, Kuldeep aut Kumar, Ajay aut Biswas, Bhaskar aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 24(2019), 13 vom: 28. Nov., Seite 10181-10203 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:24 year:2019 number:13 day:28 month:11 pages:10181-10203 https://doi.org/10.1007/s00500-019-04533-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 24 2019 13 28 11 10181-10203 |
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10.1007/s00500-019-04533-y doi (DE-627)OLC2034909623 (DE-He213)s00500-019-04533-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Singh, Shashank Sheshar verfasserin (orcid)0000-0003-0909-2258 aut ACO-IM: maximizing influence in social networks using ant colony optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. Information diffusion Influence maximization Social networks Ant colony optimization Singh, Kuldeep aut Kumar, Ajay aut Biswas, Bhaskar aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 24(2019), 13 vom: 28. Nov., Seite 10181-10203 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:24 year:2019 number:13 day:28 month:11 pages:10181-10203 https://doi.org/10.1007/s00500-019-04533-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 24 2019 13 28 11 10181-10203 |
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aco-im: maximizing influence in social networks using ant colony optimization |
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ACO-IM: maximizing influence in social networks using ant colony optimization |
| abstract |
Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstractGer |
Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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ACO-IM: maximizing influence in social networks using ant colony optimization |
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Singh, Kuldeep Kumar, Ajay Biswas, Bhaskar |
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