Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks
In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Niu, Min [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: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study - Dai, Jiamiao ELSEVIER, 2022, europhysics journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:506 ; year:2018 ; day:15 ; month:09 ; pages:707-717 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.physa.2018.04.087 |
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ELV043347738 |
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| 520 | |a In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . | ||
| 520 | |a In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . | ||
| 650 | 7 | |a Average weighted shortest path |2 Elsevier | |
| 650 | 7 | |a Average receiving time |2 Elsevier | |
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10.1016/j.physa.2018.04.087 doi GBV00000000000653.pica (DE-627)ELV043347738 (ELSEVIER)S0378-4371(18)30515-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Niu, Min verfasserin aut Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network Elsevier Song, Shuaishuai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:506 year:2018 day:15 month:09 pages:707-717 extent:11 https://doi.org/10.1016/j.physa.2018.04.087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 506 2018 15 0915 707-717 11 |
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10.1016/j.physa.2018.04.087 doi GBV00000000000653.pica (DE-627)ELV043347738 (ELSEVIER)S0378-4371(18)30515-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Niu, Min verfasserin aut Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network Elsevier Song, Shuaishuai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:506 year:2018 day:15 month:09 pages:707-717 extent:11 https://doi.org/10.1016/j.physa.2018.04.087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 506 2018 15 0915 707-717 11 |
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10.1016/j.physa.2018.04.087 doi GBV00000000000653.pica (DE-627)ELV043347738 (ELSEVIER)S0378-4371(18)30515-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Niu, Min verfasserin aut Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network Elsevier Song, Shuaishuai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:506 year:2018 day:15 month:09 pages:707-717 extent:11 https://doi.org/10.1016/j.physa.2018.04.087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 506 2018 15 0915 707-717 11 |
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10.1016/j.physa.2018.04.087 doi GBV00000000000653.pica (DE-627)ELV043347738 (ELSEVIER)S0378-4371(18)30515-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Niu, Min verfasserin aut Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network Elsevier Song, Shuaishuai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:506 year:2018 day:15 month:09 pages:707-717 extent:11 https://doi.org/10.1016/j.physa.2018.04.087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 506 2018 15 0915 707-717 11 |
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10.1016/j.physa.2018.04.087 doi GBV00000000000653.pica (DE-627)ELV043347738 (ELSEVIER)S0378-4371(18)30515-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Niu, Min verfasserin aut Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network Elsevier Song, Shuaishuai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:506 year:2018 day:15 month:09 pages:707-717 extent:11 https://doi.org/10.1016/j.physa.2018.04.087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 506 2018 15 0915 707-717 11 |
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Enthalten in Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study Amsterdam volume:506 year:2018 day:15 month:09 pages:707-717 extent:11 |
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610 VZ 44.91 bkl Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network Elsevier |
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ddc 610 bkl 44.91 Elsevier Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network |
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ddc 610 bkl 44.91 Elsevier Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network |
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ddc 610 bkl 44.91 Elsevier Average weighted shortest path Elsevier Average receiving time Elsevier Weighted Cayley network |
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Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study |
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Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study |
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Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks |
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Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks |
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Niu, Min |
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Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study |
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Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study |
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scaling of average weighted shortest path and average receiving time on the weighted cayley networks |
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Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks |
| abstract |
In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . |
| abstractGer |
In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . |
| abstract_unstemmed |
In this paper, we mainly study two important properties of weighted Cayley networks depending on a weight factor r . The weight factor makes it more difficult to calculate the topological characteristics such as average weighted shortest path (AWSP) and average receiving time (ART) of the networks. We first calculate some basic network measurements such as average degree and average node strength. Then we derive the expression for AWSP and show that it stays bounded with network order growing ( 0 < r < 1 ) in the limit of large network order. At last, we focus on random walks and trapping issue on the networks. That is, we calculate the ART. In more detail: for 0 < r < 1 2 , the ART grows with increasing size N t as ln N t ; for r = 1 2 , the ART grows with increasing size N t as ln 2 N t ; for 1 2 < r < 1 , the ART grows sublinearly with the network size N t ; for r = 1 , the ART grows linearly with the network size N t . |
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Scaling of average weighted shortest path and average receiving time on the weighted Cayley networks |
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