مقاله انگلیسی رایگان در مورد رنکینگ نویسندگان در شبکه های اجتماعی علمی – امرالد ۲۰۱۸

emerald

 

مشخصات مقاله
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۳۳ صفحه
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منتشر شده در نشریه امرالد
نوع مقاله ISI
عنوان انگلیسی مقاله Ranking authors in academic social networks: a survey
ترجمه عنوان مقاله رنکینگ نویسندگان در شبکه های اجتماعی علمی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی فناوری اطلاعات
گرایش های مرتبط اینترنت و شبکه های گسترده
مجله International Islamic University – Islamabad – Pakistan
دانشگاه کتابخانه های تیچ – Library Hi Tech
کلمات کلیدی شبکه های اجتماعی آکادمیک، رتبه بندی نویسنده، پیدا کردن کارشناسان، رتبه بندی مبتنی بر یادگیری، تجزیه و تحلیل لینک، رتبه بندی همسانی متن
کلمات کلیدی انگلیسی Academic social networks, Author ranking, Expert finding, Learning-based ranking, Link analysis, Text similarity ranking
کد محصول E6130
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۱٫ Introduction

With the emergence of social network, the world has become a very small place where people are connected to each other via satellite channels, wireless communications 3G/4G networks and many more. We can define a social network as a network within which individuals and/or organizations are arranged as nodes (called actors) and are largely interconnected via edges signifying various relationships, for example, co-authorship, citations, references, recommendation, friendship, likes and dislikes, etc. Representative social networks that are very popular include Twitter, Facebook, Flickr, Instagram, YouTube, etc. Social networks are often represented as graph structures to facilitate mining and analysis of the networks. Academic social networks (ASNs) are a subclass of social networks with scientific researchers as the main actors who collaborate in a research and appear as co-authors of publications. Such networks are now materialized on the internet and are well supported by various social networking service platforms. Many online publication repositories, such as Citeseer[1] and DBLP[2] are good examples of materialized ASNs on the Internet. They are frequently used for various mining tasks such as author ranking and expert recommendation. With production of a large number of scientific articles, finding relevant information has become a problem in recent years. With an exponential increase in the size of the data, growing computational powers and economical storage mechanism, the problem of finding relevant information has gathered attention of the researchers. With an increase in the size of scholarly data, ranking in ASNs has also become an integral part of these networks. These methods are required for expert finding, research grant recommendations, finding relevant reviewers and members for editorial panels of journals, workshops and conferences, faculty promotions and relevant tasks in ASNs. There are some intrinsic problems that are involved in ranking of ASNs. These include the dependability of the results of ranking with the attributes used as ranking criteria. Review of these ranking criteria like the number of publications, the number of citations, the citation date, the context of citations, the prestige of authors of citing article topic sensitivity, temporal dimension and so forth would throw light on the role of these ranking criteria.

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