مقاله انگلیسی رایگان در مورد پیدا کردن گره های نفوذ در شبکه های اجتماعی

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مشخصات مقاله
تعداد صفحات مقاله انگلیسی ۱۰ صفحه
هزینه  دانلود مقاله انگلیسی رایگان میباشد.
عنوان انگلیسی مقاله Discovering Influential Nodes in Social Networks through Community Finding
ترجمه عنوان مقاله پیدا کردن گره های نفوذ در شبکه های اجتماعی از طریق جستجوی عمومی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی فناوری اطلاعات
گرایش های مرتبط اینترنت و شبکه های گسترده، شبکه های کامپیوتری
دانشگاه Grand Valley State University – Allendale – USA
کد محصول E6131
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۱ INTRODUCTION

In a social network, nodes are often capable of exerting influence over the other nodes to which they are linked. An influenced node will take on a behavior or characteristic of a linked node. For example, a person who is friends with a number of people who visit a particular news website is more likely to start visiting the website as well. Some nodes in a network will be more influential than others. Networks are generally not homogeneous. Nodes of similar types are often found grouped together in localized areas of the network. Community finding algorithms are designed to identify such groups. In this paper, using community finding, we investigate how localized influence is and how we can use communities to find influential nodes.

۱٫۱ Influential Nodes Finding nodes that are highly influential is of interest to managers and analysts who work with social networks. Marketing managers may want to find influential people to offer them a discount or free product hoping that they will convince their friends to buy the product. Political operatives are also interested in finding these influential people to help them to spread their message. Researchers have studied and developed models to simulate how influence is spread throughout a network (Goldenberg et al., 2001; Granovetter, 1978). The same diffusion models that are used for influence can also be applied to the spread of infectious disease. Infected influential nodes are capable of infecting a larger portion of the population than those that are less influential. Thus, public health officials might also be interested in issuing inoculations to influential nodes. A number of algorithms have been proposed to find influential nodes, among them the probabilistic model of Domingos and Richardson (Domingos and Richardson, 2001) and the greedy approach by Kempe, et al. (Kempe et al., 2003). In this paper, we consider the later approach as it allows the number of nodes to be specified, which is important for comparisons. It is also simpler in that it requires only a network graph and the number of influential nodes desired as input. The Domingos/Richardson model requires associated cost and revenue amounts.

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