تلفن: ۰۴۱۴۲۲۷۳۷۸۱
تلفن: ۰۹۲۱۶۴۲۶۳۸۴

دانلود رایگان مقاله انگلیسی روش تشخیص رهبر – اجتماع برای تشخیص اجتماع در شبکه های اجتماعی – نشریه الزویر

عنوان فارسی: روش مقیاس پذیر و جدید تشخیص رهبر - اجتماع برای تشخیص اجتماع در شبکه های اجتماعی
عنوان انگلیسی: A new scalable leader-community detection approach for community detection in social networks
تعداد صفحات مقاله انگلیسی : 9 تعداد صفحات ترجمه فارسی : ترجمه نشده
سال انتشار : 2018 نشریه : الزویر - Elsevier
فرمت مقاله انگلیسی : PDF کد محصول : E5837
محتوای فایل : PDF حجم فایل : mb 3
رشته های مرتبط با این مقاله: مهندسی فناوری اطلاعات و علوم اجتماعی
گرایش های مرتبط با این مقاله: شبکه های کامپیوتری
مجله: شبکه های اجتماعی - Social Networks
دانشگاه: Laboratory of Information and Communication Technologies - National School of Applied Sciences - Morocco
کلمات کلیدی: رهبر، تشخیص جامعه، نمودار بزرگ، مرکزیت، شبکه اجتماعی، شباهت، داده های بزرگ، تئوری گراف
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چکیده

abstract

Studying social influence in networks is crucialto understand how behavior spreads. An interesting number of theories were elaborated to analyze how innovations and trends get adopted. The traditional view assumes that a minority of members in a society possess qualities that make them exceptionally persuasive in spreading ideas to others. These exceptional individuals drive trends on behalf of the majority of ordinary people. They are loosely described as being informed, respected, and well connected. The leaders or influential are responsible for the dissemination of information and the propagation of influence. In this paper, we propose a new scalable and a deterministic approach for the detection of communities using leaders nodes named Leader-Community Detection Approach LCDA. The proposed approach has two main steps. The first step is the leaders’ retrieval. The second step is the community detection using similarity between nodes. Our algorithms provide good results compared to ground truth membership community.

نتیجه گیری

5. Conclusion

The Leader-Community Detection Algorithms are introduced for finding community structure in social networks. The proposed approach shows a major advantage, which does not require prior knowledge of the number of leaders and communities. The proposed approach begins with leader detection to find the most influential nodes of the network, followed by community detection. For each detected leader, its community is built by seeking similar nodes. Experimental results demonstrate that the performance of the proposed algorithms LCDA1 and LCDA2 is reliable in terms of accuracy and finding community structure based on modularity, NMI and ARI. While the ARI values are not as reliable for larger size networks, it still represents a significant improvement relative to previous versions of LeadersRank algorithms.