مدلهای احتمالی کاربر محور برای انتشار محتوا در وبلاگ‌نویسی
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مدلهای احتمالی کاربر محور برای انتشار محتوا در وبلاگ‌نویسی

عنوان فارسی مقاله: مدلهای احتمالی کاربر محور برای انتشار محتوا در وبلاگ‌نویسی
عنوان انگلیسی مقاله: User-centered probabilistic models for content diffusion in the blogosphere
مجله/کنفرانس: رسانه ها و شبکه های اجتماعی آنلاین - Online Social Networks and Media
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: سامانه های شبکه ای، اینترنت و شبکه های گسترده، برنامه نویسی کامپیوتر، مهندسی نرم افزار
کلمات کلیدی فارسی: انتشار اطلاعات، مدل سازی، شبکه های اجتماعی
کلمات کلیدی انگلیسی: Information diffusion، Modelization، Social networks
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.osnem.2018.01.001
دانشگاه: University Grenoble Alpes, CNRS, Grenoble INP, LIG, France
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
شناسه ISSN: 2468-6964
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 15
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E11221
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Notations and problem statement


4- Content diffusion models


5- Experimental validation


6- Influence maximization (IM)


7- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Predicting the diffusion of information in social networks is a key problem for applications like Opinion Leader Detection, Buzz Detection or Viral Marketing. Many diffusion models are direct extensions of the Cascade and Threshold models, initially proposed for epidemiology and social studies. In such models, the diffusion process is based on the dynamics of interactions between neighbor nodes in the network (the social pressure), and largely ignores important dimensions as the content diffused and the active/passive role users tend to have in social networks. We propose here a new family of models that aims at predicting how a content diffuses in a network by making use of additional dimensions: the content diffused, user’s profile and willingness to diffuse. In particular, we show how to integrate these dimensions into simple feature functions, and propose a probabilistic modeling to account for the diffusion process. These models are then illustrated and compared with other approaches on two blog datasets. The experimental results obtained on these datasets show that taking into account the content diffused is important to accurately model the diffusion process. Lastly, we study the influence maximization problem with these models and prove that it is NP-hard, prior to propose an adaptation of the greedy algorithm to approximate the optimal solution.


Introduction


Propagation models in content networks, i.e. social networks in which content are shared and diffused among users, aim at reproducing the diffusion of information between users. Being able to accurately model this diffusion has several practical applications, as the identification of influence hubs, the choice of initial diffusers for a maximal diffusion, or the identification of links one has to remove in order to limit the diffusion (e.g. for stopping rumors). Most of the models proposed in the domain of information diffusion are extensions of the Independent Cascade model (IC) [1] and the Linear Threshold model (LT) [2]. The IC model is based on the following simple principle: as soon as a user (i.e. a node in the social network) nj is infected, she has a unique chance to infect each of her direct neighbors ni with a probability Pji that depends on both nj and ni. The LT model considers that a node ni of the social network (i.e. a user) is contaminated if the sum of the weights on its incoming edges are above a threshold θi specific to ni, this threshold being chosen randomly in many instances of the model [3]. They nevertheless fail to take into account for two important elements: • They ignore the content of the information diffused even though, in a given social network, two different pieces of information will not propagate in the same way; • They tend to ignore users characteristics even though the interest of a particular user plays a major role in the diffusion process. Ignoring the content being diffused entails that, in these models, different contents issued from the same user will diffuse in the same manner. In other words, in content-agnostic models, the diffusion cascades1 originating from a given user are the same, regardless of the content being diffused.

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