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Статті в журналах з теми "Personal networks"

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Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 квітня 2022

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1

Huang, Gang, and Mark Tausig. "Network range in personal networks." Social Networks 12, no. 3 (September 1990): 261–68. http://dx.doi.org/10.1016/0378-8733(90)90009-x.

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2

HAYTHORNTHWAITE, CAROLINE. "Online Personal Networks." New Media & Society 2, no. 2 (June 2000): 195–226. http://dx.doi.org/10.1177/14614440022225779.

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3

Zahariadis, T. "Personal area networks." Communications Engineer 1, no. 3 (June 1, 2003): 12–15. http://dx.doi.org/10.1049/ce:20030301.

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4

Boase, Jeffrey. "PERSONAL NETWORKS AND THE PERSONAL COMMUNICATION SYSTEM." Information, Communication & Society 11, no. 4 (June 2008): 490–508. http://dx.doi.org/10.1080/13691180801999001.

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5

HARADA, Ken, and Hidehiro SUGISAWA. "Urbanism and Personal Networks:." Japanese Sociological Review 65, no. 1 (2014): 80–96. http://dx.doi.org/10.4057/jsr.65.80.

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6

Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Understanding Personal Area Networks." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (May 30, 2017): 628–29. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0207.

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7

GOODMAN, DAVID J. "Wireless Personal Communication Networks." Optics and Photonics News 3, no. 2 (February 1, 1992): 8. http://dx.doi.org/10.1364/opn.3.2.000008.

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8

Bisdikian, C., P. Bhogwat, and N. Golmie. "Wireless personal area networks." IEEE Network 15, no. 5 (September 2001): 10–11. http://dx.doi.org/10.1109/mnet.2001.953228.

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9

Braley, Richard C., Ian C. Gifford, and Robert F. Heile. "Wireless personal area networks." ACM SIGMOBILE Mobile Computing and Communications Review 4, no. 1 (January 2000): 26–33. http://dx.doi.org/10.1145/360449.360465.

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10

van der Poel, Mart G. M. "Delineating personal support networks." Social Networks 15, no. 1 (March 1993): 49–70. http://dx.doi.org/10.1016/0378-8733(93)90021-c.

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11

Schumny, Harald. "Wireless personal area networks." Computer Standards & Interfaces 26, no. 3 (May 2004): 157–58. http://dx.doi.org/10.1016/j.csi.2003.12.004.

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12

Hofte, G. Henri ter, and Ingrid Mulder. "Dynamic personal social networks." ACM SIGGROUP Bulletin 24, no. 3 (December 2003): 139–42. http://dx.doi.org/10.1145/1052829.1052830.

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13

Tronca, Luigi. "I personal network in Italia." SOCIOLOGIA E POLITICHE SOCIALI, no. 2 (July 2012): 55–84. http://dx.doi.org/10.3280/sp2012-002004.

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Анотація:

This article provides an analysis of the main features of the personal networks capable of providing social support in Italy. The first part of the paper presents a theoretical and methodological discussion about the potentials and limits of the personal network analysis. In the second part, the author presents the method used in carrying out a survey on personal networks of a sample of the population residing in Italy. Through the use of the name generator, name interpreter, and name interrelator questionnaires, this survey enables to reconstruct the size, the contents, and the form of the personal networks of respondents. The contents analyzed are material resources, social credentials, contacts, and psychological support. The forms are studied through the concepts of structural holes and network closure. Moreover the personal networks are divided into the following social circles: relatives, friends, and acquaintances. The research was conducted on a sample of over 1.200 individuals. In the last part of the article the results of this research are presented and discussed. One of the findings of this research is that Italy is divided into two geographical areas, with respect to the presence of the personal networks of support: the inhabitants of Central and Northern Italy have personal networks much more extensive than the personal networks of the inhabitants of Southern Italy and of Italian Islands.

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14

McCornack, Steven. "Personal relationships and personal networks - by Malcolm Parks." Journal of Communication 58, no. 2 (June 2008): 397–99. http://dx.doi.org/10.1111/j.1460-2466.2008.00391_2.x.

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15

Wojcik, Stefan. "Why Legislative Networks? Analyzing Legislative Network Formation." Political Science Research and Methods 7, no. 3 (December 4, 2017): 505–22. http://dx.doi.org/10.1017/psrm.2017.37.

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Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.

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16

Jyothirmai, Pondi, Jennifer S. Raj, and S. Smys. "Secured Self Organizing Network Architecture in Wireless Personal Networks." Wireless Personal Communications 96, no. 4 (May 30, 2017): 5603–20. http://dx.doi.org/10.1007/s11277-017-4436-4.

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17

Marcum, Christopher S., Jielu Lin, and Laura M. Koehly. "AGE DIFFERENCES IN COGNITIVE PERSONAL NETWORKS." Innovation in Aging 3, Supplement_1 (November 2019): S177. http://dx.doi.org/10.1093/geroni/igz038.629.

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Abstract Previous research has found a negative association between network size and age, suggesting that people experience greater isolation with advancing age. In this paper, we evaluate age differences in how individuals perceive their social worlds to be structured, rather than focusing solely on network size. A nationally represented sample of respondents (n=1,824) reported on their own ties to their close personal network members (i.e., ego-alter ties) as well as their perceptions of acquaintanceship between those members (i.e., alter-alter ties). We used social network analysis to assess how the structure of these relationships vary by respondent age. We find a positive association between respondent age and personal network size and a negative association between network members’ ages and the number of ties respondents’ perceive their members to have to each other. This effect significantly weakens as respondent age increases. Moreover, we find evidence of age-homophily, intergenerational contact spanning three generations in both ego-alter and alter-alter ties, and age differences in ego network composition. Our results suggest that the evolution of our social worlds across the life course shifts in terms of size and structure. While contemporary close personal networks may grow slightly with age, perceived social ties among one’s network members become less cohesive and less diverse with age. We discuss these results in the context of recent findings that suggest aging uniformly insulates individuals from social contact from both structural and symbolic perspectives.

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18

Wyngaerden, François, Marie Tempels, Jean-Louis Feys, Vincent Dubois, and Vincent Lorant. "The personal social network of psychiatric service users." International Journal of Social Psychiatry 66, no. 7 (June 5, 2020): 682–92. http://dx.doi.org/10.1177/0020764020927447.

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Background: For psychiatric service users, the personal social network offers resources such as behavioural guidance, social support, and coherence of care delivery. So far, most research on the subject has assessed the availability of these resources using size and composition measures. However, the availability of network resources also depends on the cohesion of the relationships between network members, a topic that is rarely addressed in the literature. Aims: In this article, we aim to describe the cohesion of psychiatric service users’ networks. Method: We carried out a personal network survey and collected data on the social networks of 380 service users recruited in outpatient and inpatient services in Belgium. We used an ego-network mapping technique. The data were analysed using several structural metrics describing size, composition, and cohesion. We carried out analysis of variance (ANOVA) in relation to residential status, level of education, psychiatric history, and psychosocial functioning and analysed three cohesion indicators (density, fragmentation, and centralisation) with regression analyses. Results: Personal social networks were small and not very cohesive. Most were composed of a dense subgroup as well as several isolated network members. The analyses revealed that highly educated psychiatric service users had more fragmented networks, while users living in independent accommodation had less dense networks. Density and fragmentation were not associated with the severity of psychosocial functioning, whereas centralisation was. Conclusions: The low level of cohesion may indicate that service users aim to access multiple and diverse social resources and that better-off service users are more successful at doing so. On the whole, however, these personal social networks were fragile, because they contained a high number of isolated network members. Finally, it could be beneficial for professionals to pay special attention to the central persons within the networks of psychiatric service users.

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19

Ishida, Mitsunori. "The Diversity of Personal Networks." Annual review of sociology 2001, no. 14 (2001): 126–38. http://dx.doi.org/10.5690/kantoh.2001.126.

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20

Harada, Ken. "Social Stratification and Personal Networks." Iryo To Shakai 22, no. 1 (2012): 57–68. http://dx.doi.org/10.4091/iken.22.57.

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21

Miller, Michelle M. "Social Networks and Personal Bankruptcy." Journal of Empirical Legal Studies 12, no. 2 (April 28, 2015): 289–310. http://dx.doi.org/10.1111/jels.12073.

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22

Błasik, Patryk. "Social Networks and Personal Security." ASEJ Scientific Journal of Bielsko-Biala School of Finance and Law 22, no. 3 (October 15, 2018): 9–12. http://dx.doi.org/10.5604/01.3001.0012.7450.

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Анотація:

The emergence of the Internet caused a significant simplification of interpersonal communication all over the world. True, the communication became fast, cheap and convenient and for people, who by nature are social beings, it enabled easy bonding, maintaining relationships and strengthened the feeling of belonging to one group or another. Along with development of the Internet social networking portals emerged becoming a popular means of communication and entertainment specifically dedicated to young people. The paper outlines the most important advantages and disadvantages of social networks especially with respect to security.

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23

Lubbers, Miranda J., José Luis Molina, and Christopher McCarty. "Personal Networks and Ethnic Identifications." International Sociology 22, no. 6 (December 2007): 721–41. http://dx.doi.org/10.1177/0268580907082255.

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24

Wang, Zhongqing, Shoushan Li, and Guodong Zhou. "Personal summarization from profile networks." Frontiers of Computer Science 11, no. 6 (July 13, 2016): 1085–97. http://dx.doi.org/10.1007/s11704-016-5088-3.

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25

Gu, Yanying, R. Venkatesha Prasad, and Ignas Niemegeers. "Mobility Modeling for Personal Networks." Wireless Personal Communications 58, no. 2 (December 25, 2009): 169–96. http://dx.doi.org/10.1007/s11277-009-9887-9.

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26

Radenkovic, Milena. "Cognitive Privacy for Personal Clouds." Mobile Information Systems 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/7107103.

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This paper proposes a novel Cognitive Privacy (CogPriv) framework that improves privacy of data sharing between Personal Clouds for different application types and across heterogeneous networks. Depending on the behaviour of neighbouring network nodes, their estimated privacy levels, resource availability, and social network connectivity, each Personal Cloud may decide to use different transmission network for different types of data and privacy requirements. CogPriv is fully distributed, uses complex graph contacts analytics and multiple implicit novel heuristics, and combines these with smart probing to identify presence and behaviour of privacy compromising nodes in the network. Based on sensed local context and through cooperation with remote nodes in the network, CogPriv is able to transparently and on-the-fly change the network in order to avoid transmissions when privacy may be compromised. We show that CogPriv achieves higher end-to-end privacy levels compared to both noncognitive cellular network communication and state-of-the-art strategies based on privacy-aware adaptive social mobile networks routing for a range of experiment scenarios based on real-world user and network traces. CogPriv is able to adapt to varying network connectivity and maintain high quality of service while managing to keep low data exposure for a wide range of privacy leakage levels in the infrastructure.

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27

Hernández-Carrión, Carlos, Carmen Camarero-Izquierdo, and Jesús Gutiérrez-Cillán. "The internal mechanisms of entrepreneurs’ social capital: A multi-network analysis." BRQ Business Research Quarterly 23, no. 1 (January 2020): 234094442090104. http://dx.doi.org/10.1177/2340944420901047.

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The present work addresses how and to what extent the personal and professional relationship networks of small-scale local entrepreneurs help improve their scarce resource endowments. Adopting a mechanistic and functioning oriented view of social capital, the paper suggests the existence of two different mechanisms which may explain the enrichment and entrepreneurial exploitation processes of social capital’s resources: (1) the resource mechanism, based on a network’s size and diversity, providing quantity and variety of social capital resources, and (2) the exchange mechanism, based on a network’s cohesion and relational quality, favouring the interchangeability of these resources among network members. The empirical study individually explores both the personal and professional networks of 958 Spanish entrepreneurs. Findings reveal how the two mechanisms are necessary and mutually complementary, although the resource mechanism proves more advantageous when exploiting personal networks, whereas the exchange mechanism prevails in the case of professional networks. JEL CLASSIFICATION L14; L26; M13; M14

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28

Vermunt, Jeroen K., and Matthijs Kalmijn. "Random Effects Models for Personal Networks." Methodology 2, no. 1 (January 2006): 34–41. http://dx.doi.org/10.1027/1614-2241.2.1.34.

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We propose analyzing personal or ego-centered network data by means of two-level generalized linear models. The approach is illustrated with an example in which we assess whether personal networks are homogenous with respect to marital status after controlling for age homogeneity. In this example, the outcome variable is a bivariate categorical response variable (alter’s marital status and age category). We apply both factor-analytic parametric and latent-class-based nonparametric random effects models and compare the results obtained with the two approaches. The proposed models can be estimated with the Latent GOLD program for latent class analysis.

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29

Goldbarg, Rosalyn Negrón, and Emma J. Brown. "Gender, Personal Networks, and Drug Use among Rural African Americans." International Quarterly of Community Health Education 30, no. 1 (March 30, 2010): 41–54. http://dx.doi.org/10.2190/iq.30.1.d.

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One of the main unifying concepts of research examining gender variations in drug use behavior is the social network. Yet, research specifically focusing on how the social networks of these groups differ by gender is limited. Few studies have investigated the social networks of rural African Americans who use drugs. In this study, we compared the personal networks of 20 rural African-American men and women addicted to cocaine using social network analysis (SNA) methods. The data do not support strong assertions about gender differences in the personal networks of the study sample. However, the results of the study suggest that men tend to have more drug users in their networks than women, as well as less structurally cohesive networks. Women tend to include more men in their personal networks than men included women. Implications of the research results for network-based drug prevention intervention as well as the value of SNA methods for drug use research are discussed.

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30

Ganjour, Olga, Eric D. Widmer, Gil Viry, Jacques-Antoine Gauthier, Vincent Kaufmann, and Guillaume Drevon. "Understanding the reconstruction of personal networks through residential migration trajectories." Migration Letters 17, no. 5 (September 28, 2020): 621–38. http://dx.doi.org/10.33182/ml.v17i5.694.

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This article examines how residential trajectories influence the spatiality and composition of personal networks. Three mechanisms are considered: the addition of spatially close network members, the selection of spatially distant network members, and the substitution of spatially distant network members by spatially close ones. An ego-centred network analysis combined with sequence analysis of residential experiences is used to capture the personal networks and the residential trajectories of individuals from two birth cohorts in Switzerland. A series of regression models tests the association between the types of personal networks that individuals develop, in terms of both spatial dispersion and composition, and their residential trajectories. The results show that individuals who moved far away from their place of birth are embedded in large and diversified personal networks, which include spatially distant relatives, local nuclear family members, and local friends. On average, individuals who experienced residential migration have larger and more diverse personal networks than individuals who stayed close to their place of birth. The addition mechanism accounts for much of this greater diversity.

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31

Maya-Jariego, Isidro, Srebrenka Letina, and Elena González Tinoco. "Personal networks and psychological attributes: Exploring individual differences in personality and sense of community and their relationship to the structure of personal networks." Network Science 8, no. 2 (November 7, 2019): 168–88. http://dx.doi.org/10.1017/nws.2019.15.

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AbstractThe paper explores the relationship between personality characteristics, sense of community and the structure of personal networks. We collected personal network data from 100 adults, consisting of the information about their 45 alters and ties among them. In addition to the typically used bivariate correlations to analyze the relationship between individual psychological differences and network structure, we propose first the use of typologies of networks and personality and second modified versions of the triadic census in ego-networks. The results show that different personality types tend to occupy a different kind of networks and that newly introduced triadic measures show a relatively higher association with examined psychological attributes than global network measures. Overall, the Psychological Sense of Community showed higher associations with network measures than Big Five personality traits. The former was positively correlated with measures of closed triads among alters, but also with alters embedded in triads with weak ties. Regarding personality traits, Emotional Stability was positively correlated with strong closed triads and with the overall indicator of density. The elaboration of typologies and the census of triads are shown to be effective strategies for the description of personal networks, as well as for the analysis of individual psychological differences.

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32

Requena, Felix, and Luis Ayuso. "Individualism or complementarity? The effect of digital personal networks on face-to-face personal networks." Information, Communication & Society 22, no. 14 (June 5, 2018): 2097–111. http://dx.doi.org/10.1080/1369118x.2018.1477968.

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33

Rejeb‐Khachlouf, Nada, Lassaâd Mezghani, and Bertrand Quélin. "Personal networks and knowledge transfer in inter‐organizational networks." Journal of Small Business and Enterprise Development 18, no. 2 (May 17, 2011): 278–97. http://dx.doi.org/10.1108/14626001111127070.

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34

Bahulkar, Ashwin, Boleslaw K. Szymanski, Nitesh Chawla, Omar Lizardo, and Kevin Chan. "Influence of Personal Preferences on Link Dynamics in Social Networks." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/4543563.

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Анотація:

We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.

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35

Louati, W., and D. Zeghlache. "Network-based virtual personal overlay networks using programmable virtual routers." IEEE Communications Magazine 43, no. 8 (August 2005): 86–94. http://dx.doi.org/10.1109/mcom.2005.1497558.

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36

Sakata, Shiro. "Trend on Wireless Personal Area Networks." IEICE Communications Society Magazine 2007, no. 2 (2007): 2_44–2_54. http://dx.doi.org/10.1587/bplus.2007.2_44.

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37

Bidart, Claire, and Alain Degenne. "Introduction: the dynamics of personal networks." Social Networks 27, no. 4 (October 2005): 283–87. http://dx.doi.org/10.1016/j.socnet.2004.11.006.

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38

Bidart, Claire, Alain Degenne, and Michel Grossetti. "Personal networks typologies: A structural approach." Social Networks 54 (July 2018): 1–11. http://dx.doi.org/10.1016/j.socnet.2017.11.003.

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39

Fischer, H., B. Kneifel, A. Gellweiler, A. Bolz, and M. Gmelin. "Personal medical data in public networks." Minimally Invasive Therapy & Allied Technologies 11, no. 2 (January 2002): 41–47. http://dx.doi.org/10.1080/136457002753632448.

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40

Jackson, Paul R. "Personal networks, support mobilization and unemployment." Psychological Medicine 18, no. 2 (May 1988): 397–404. http://dx.doi.org/10.1017/s0033291700007947.

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SynopsisMeasures were obtained of features of social network structure and the mobilization of instrumental and expressive support from a sample of 301 unemployed men. Findings showed dependence on a restricted number of family members and close friends, with high levels of expressive support (for example, 71 per cent of the sample reported that someone had ‘let you know that he or she is always around if you need help with something’). Follow-up interviews were obtained on two further occasions over a period of one year, and significant changes in network structure and support mobilization were found. A decrease in non-family network size suggested greater reliance on close family and friends and a withdrawal from weaker ties with members of the community. Finally, the findings of this study emphasize the importance of incorporating a time dimension into research on the mobilization of support following a stressful life event such as job loss.

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41

Ananasso, Fulvio, and Francesco Delli Priscoli. "Satellite systems for personal communication networks." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 423. http://dx.doi.org/10.1016/s0920-5489(99)90834-6.

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42

Haas, Mary RC, Kellen Haley, Bella S. Nagappan, Felix Ankel, Anand Swaminathan, and Sally A. Santen. "The connected educator: personal learning networks." Clinical Teacher 17, no. 4 (February 24, 2020): 373–77. http://dx.doi.org/10.1111/tct.13146.

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43

Gaskell, P. S. "Developing technologies for personal communication networks." Electronics & Communications Engineering Journal 4, no. 2 (1992): 53. http://dx.doi.org/10.1049/ecej:19920013.

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44

McCarty, C., H. R. Bernard, P. D. Killworth, G. A. Shelley, and E. C. Johnsen. "Eliciting representative samples of personal networks." Social Networks 19, no. 4 (October 1997): 303–23. http://dx.doi.org/10.1016/s0378-8733(96)00302-4.

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45

Nussbaum-Beach, Sheryl. "Just the Facts: Personal Learning Networks." Educational Horizons 91, no. 2 (December 2012): 26–27. http://dx.doi.org/10.1177/0013175x1209100208.

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46

Kowald, Matthias, and Kay W. Axhausen. "Surveying data on connected personal networks." Travel Behaviour and Society 1, no. 2 (May 2014): 57–68. http://dx.doi.org/10.1016/j.tbs.2013.11.001.

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