Adaptive influence maximization under fixed observation time-step
The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Yapu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries - Schweiss, Rüdiger ELSEVIER, 2015transfer abstract, the journal of the EATCS, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:928 ; year:2022 ; day:3 ; month:09 ; pages:104-114 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.tcs.2022.06.018 |
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ELV058586180 |
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| 520 | |a The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. | ||
| 520 | |a The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. | ||
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10.1016/j.tcs.2022.06.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001864.pica (DE-627)ELV058586180 (ELSEVIER)S0304-3975(22)00383-8 DE-627 ger DE-627 rakwb eng 620 VZ 690 VZ 50.92 bkl Zhang, Yapu verfasserin aut Adaptive influence maximization under fixed observation time-step 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm Elsevier Chen, Shengminjie oth Xu, Wenqing oth Zhang, Zhenning oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:928 year:2022 day:3 month:09 pages:104-114 extent:11 https://doi.org/10.1016/j.tcs.2022.06.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 928 2022 3 0903 104-114 11 |
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10.1016/j.tcs.2022.06.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001864.pica (DE-627)ELV058586180 (ELSEVIER)S0304-3975(22)00383-8 DE-627 ger DE-627 rakwb eng 620 VZ 690 VZ 50.92 bkl Zhang, Yapu verfasserin aut Adaptive influence maximization under fixed observation time-step 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm Elsevier Chen, Shengminjie oth Xu, Wenqing oth Zhang, Zhenning oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:928 year:2022 day:3 month:09 pages:104-114 extent:11 https://doi.org/10.1016/j.tcs.2022.06.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 928 2022 3 0903 104-114 11 |
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10.1016/j.tcs.2022.06.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001864.pica (DE-627)ELV058586180 (ELSEVIER)S0304-3975(22)00383-8 DE-627 ger DE-627 rakwb eng 620 VZ 690 VZ 50.92 bkl Zhang, Yapu verfasserin aut Adaptive influence maximization under fixed observation time-step 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm Elsevier Chen, Shengminjie oth Xu, Wenqing oth Zhang, Zhenning oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:928 year:2022 day:3 month:09 pages:104-114 extent:11 https://doi.org/10.1016/j.tcs.2022.06.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 928 2022 3 0903 104-114 11 |
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10.1016/j.tcs.2022.06.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001864.pica (DE-627)ELV058586180 (ELSEVIER)S0304-3975(22)00383-8 DE-627 ger DE-627 rakwb eng 620 VZ 690 VZ 50.92 bkl Zhang, Yapu verfasserin aut Adaptive influence maximization under fixed observation time-step 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm Elsevier Chen, Shengminjie oth Xu, Wenqing oth Zhang, Zhenning oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:928 year:2022 day:3 month:09 pages:104-114 extent:11 https://doi.org/10.1016/j.tcs.2022.06.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 928 2022 3 0903 104-114 11 |
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10.1016/j.tcs.2022.06.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001864.pica (DE-627)ELV058586180 (ELSEVIER)S0304-3975(22)00383-8 DE-627 ger DE-627 rakwb eng 620 VZ 690 VZ 50.92 bkl Zhang, Yapu verfasserin aut Adaptive influence maximization under fixed observation time-step 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm Elsevier Chen, Shengminjie oth Xu, Wenqing oth Zhang, Zhenning oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:928 year:2022 day:3 month:09 pages:104-114 extent:11 https://doi.org/10.1016/j.tcs.2022.06.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 928 2022 3 0903 104-114 11 |
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Enthalten in Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries Amsterdam [u.a.] volume:928 year:2022 day:3 month:09 pages:104-114 extent:11 |
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Enthalten in Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries Amsterdam [u.a.] volume:928 year:2022 day:3 month:09 pages:104-114 extent:11 |
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Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
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Zhang, Yapu @@aut@@ Chen, Shengminjie @@oth@@ Xu, Wenqing @@oth@@ Zhang, Zhenning @@oth@@ |
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| author |
Zhang, Yapu |
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620 VZ 690 VZ 50.92 bkl Adaptive influence maximization under fixed observation time-step Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm Elsevier |
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ddc 620 ddc 690 bkl 50.92 Elsevier Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm |
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ddc 620 ddc 690 bkl 50.92 Elsevier Non-adaptive strategies Elsevier Adaptive influence maximization Elsevier Social network Elsevier Approximation algorithm |
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Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
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Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
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Adaptive influence maximization under fixed observation time-step |
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Adaptive influence maximization under fixed observation time-step |
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Zhang, Yapu |
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Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
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Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
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10.1016/j.tcs.2022.06.018 |
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adaptive influence maximization under fixed observation time-step |
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Adaptive influence maximization under fixed observation time-step |
| abstract |
The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. |
| abstractGer |
The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. |
| abstract_unstemmed |
The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies. |
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Adaptive influence maximization under fixed observation time-step |
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https://doi.org/10.1016/j.tcs.2022.06.018 |
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Chen, Shengminjie Xu, Wenqing Zhang, Zhenning |
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