مقاله انگلیسی رایگان در مورد روش شناسایی اثرات مخرب در شبکه های اجتماعی – الزویر ۲۰۱۸

elsevier

 

مشخصات مقاله
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۱۷ صفحه
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نوع مقاله ISI
عنوان انگلیسی مقاله Alternative estimation methods for identifying contagion effects in dynamic social networks: A latent-space adjusted approach
ترجمه عنوان مقاله روش برآورد جایگزین برای شناسایی اثرات مخرب در شبکه های اجتماعی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی فناوری اطلاعات
گرایش های مرتبط اینترنت و شبکه های گسترده، رایانش امن
مجله شبکه های اجتماعی – Social Networks
دانشگاه Grado Department of Industrial and System Engineering – Virginia Polytechnic Institute and State University – United States
کلمات کلیدی اثرات مخرب، نفوذ اجتماعی، روش های برآورد، رویکرد فضا – پنهان
کد محصول E5838
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۱٫ Introduction

Endogenous social effects, which have long been central to the field of social science (Asch, 1952; Merton, 1957; Erbring and Young, 1979; Bandura, 1986), are defined as the propensity for the behavior of an individual to vary along with the prevalence of that behavior in some reference group containing the individual (Manski, 1993). Within the framework of social network analysis, the endogenous social effects are also known as “contagion” or “social influence”, and the reference group can be one’s network neighborhood. Contagion effects have also received much attention and have been widely studied (Kandel, 1978; Marsden and Friedkin, 1993; Doreian, 2001; An, 2011) as they have various implications for issues such as health behavior (e.g. obesity and smoking), information diffusion, or change in teacher practices, among others (Christakis and Fowler, 2007, 2008; Valente, 1995, 1996; Frank et al., 2004). However, these types of contagion effects are usually difficult to identify, as it is difficult to separate such influences from other processes when there is network autocorrelation in the data, i.e. when we observe that people who are closely related to each other tend to be similar in some salient individual behavior and attitude dimensions, it is difficult to tell which is the underlying mechanism that generates these patterns. It could be influence and contagion (Friedkin and Johnsen, 1999; Friedkin, 2001; Oetting and Donnermeyer, 1998) whereby actors assimilate the behavior of their network members; or selection mechanisms, more specifically homophily (Lazarsfeld and Merton, 1954; McPherson and Smith-Lovin, 1987; McPherson et al., 2001), where actors seek to interact with similar others; or it could be due to different social contexts where people with previous similarities can select themselves into the same social setting, and actual friendship formation just reflects the opportunities of meeting in this social setting (Feld, 1981, 1982; Kalmijn and Flap, 2001).1 Several notable attempts that try to identify contagion effects include modeling the coevolution of selection and influence (Snijders et al., 2007; Steglich et al., 2010), using indirect ties from third parties as instrumental variables (Bramoullé et al., 2009; An, 2011), or Propensity Score Matching (Aral et al., 2009). But there is still considerable misconception about when it is problematic to identify contagion effects, and why these methods would need to be applied. Furthermore, all the methods mentioned above require some form of strong assumptions such as the exponential-family parametric assumption, the standard IV assumption, the assumption that all of the dependence is captured by observable covariates, and so on, each of which imposes substantial limits on the forms of data where these methods can actually be applied.

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