برآورد تلفیقی احتمالات تأثیر برای مسأله بیشینه‌سازی گسترش تأثیر در شبکه‌های اجتماعی و کاربرد آن در صنعت برق
محورهای موضوعی : مهندسی کامپیوتر و فناوری اطلاعات
سهامه محمدی
1
,
محمد حسین ندیمی شهرکی
2
,
زهرا بهشتی
3
,
کامران زمانی فر
4
1 - دانشکده مهندسی کامپیوتر- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
2 - دانشکده مهندسی کامپیوتر- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
3 - دانشکده مهندسی کامپیوتر- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
4 - دانشکده مهندسی کامپیوتر، دانشگاه اصفهان، اصفهان، ایران.
کلید واژه: شبکه‌های اجتماعی, بیشینه‌سازی تأثیر, مدلسازی انتشار اطلاعات, احتمالات تأثیر,
چکیده مقاله :
امروزه شبکه‌های اجتماعی آنلاین ارتباط ناگسستنی با زندگی روزمره بسیاری از مردم جهان دارند. کاربرد شبکه‌های اجتماعی در کسب و کارها جهت تبلیغات، بازاریابی، سیستم‌های پیشنهاد دهنده و همچنین در سیستم‌های مدیریت مصرف منابع و انرژی رو به افزایش می‌باشد. یکی از مهم‌ترین مسائل مطرح شده در رابطه با فرایند انتشار اطلاعات در شبکه‌های اجتماعی، مسأله بیشینه‌سازی گسترش تأثیر می‌باشد. در سال‌های اخیر، تحقیق‌هایی برای بهبود کیفیت پیش‌بینی مدل‌های انتشار اطلاعات در این مسأله انجام شده است. طی بررسی‌های انجام شده در مدل‌های موجود، تخمین احتمالات تأثیر کاربران بر روی یکدیگر به طور غیر واقعی محاسبه می‌شود. در این پژوهش با هدف بررسی و بهینه‌سازی فرایند گسترش تأثیر در شبکه‌های اجتماعی، روش جدیدی برای تعیین احتمالات تأثیر در میان کاربران پیشنهاد شده است. این روش تلفیقی از دو رویکرد اصلی محاسبه احتمالات تأثیر شامل بهره‌گیری از جدول لاگ فعالیت و روش یکنواخت مقدار از پیش تعیین شده است. عملکرد روش پیشنهادی بر روی مجموعه داده‌های مختلفی از شبکه‌های اجتماعی دنیای واقعی با روش‌های رقیب مورد ارزیابی و مقایسه قرار گرفت. نتایج حاصل از آزمایش¬ها نشان می‌دهد روش پیشنهادی می‌تواند باعث افزایش کارایی پیش¬بینی‌های مورد نظر جهت حل مسائل بیشینه‌سازی گسترش تأثیر گردد.
Nowadays, online social networks have an inseparable connection with the daily life of many people in the world. The applications of social networks are increasing in businesses for advertising, marketing, and recommender systems, as well as in resource and energy consumption management systems. One of the most important problems in the information diffusion process of social networks is the influence maximization. In recent years, some research has been conducted to improve the prediction quality of information diffusion models in this problem. In a review of existing models, the influence probabilities among users are estimated unrealistically. In this research, a new method has been proposed to determine the influence probabilities among social network users. This method is a combination of two main approaches in the calculation of influence probabilities, including the use of an action log table and the uniform method with a predetermined value. The performance of the proposed method was evaluated and compared with competitive methods on different real-world social network data sets. The results of the experiments show that the proposed method can increase the efficiency of the predictions in solving the influence maximization problems.
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