ارائه روش بهبودیافته در حداکثرسازی پویایی نظرات مبتنی بر الگوریتم ژنتیک حریصانه

رعیت پرور, حسین; حسنی آهنگر, محمدرضا; سرکرده ئی, ابوالفضل

doi:10.47176/ECDJ.2025.1617

ارائه روش بهبودیافته در حداکثرسازی پویایی نظرات مبتنی بر الگوریتم ژنتیک حریصانه

نوع مقاله : مقاله پژوهشی

نویسندگان

حسین رعیت پرور ¹

محمدرضا حسنی آهنگر ²

ابوالفضل سرکرده ئی ³

¹ کارشناسی ارشد ، دانشگاه جامع امام حسین(ع)، تهران، ایران

² استاد ، دانشگاه جامع امام حسین(ع)، تهران، ایران

³ دانشجوی دکتری ، دانشگاه جامع امام حسین(ع) ، تهران ، ایران

10.47176/ECDJ.2025.1617

چکیده

تحلیل داده‌های شبکه‌های اجتماعی برخط یکی از چالش‌های علمی مورد توجه در عصر حاضر است. در زمینه تحلیل نظرات کاربران در این شبکه‌ها، حداکثرسازی پویایی نظرات به‌عنوان یک حوزه پژوهشی جدید مطرح شده است. تحقیقات پیشین در این حوزه عمدتاً فرض کرده‌اند که نظرات گره‌ها ثابت و بدون تغییر هستند. همچنین، کمتر به تغییرات زمانی نظرات کاربران پرداخته شده است. در این مقاله، با درنظر گرفتن پویایی نظرات و تغییرات آن در طول زمان، روشی برای مسئله حداکثرسازی پویایی نظرات بر اساس الگوریتم ژنتیک حریصانه پیشنهاد شده است. این روش شامل دو بخش اصلی است. مدل پویایی نظرات فعال‌شده و فرایند انتخاب گره‌های دانه. مدل پویایی نظرات فعال‌شده با استفاده از ترکیب مدل انتشار آستانه خطی و یادگیری Q بدون حالت طراحی شده است که تغییرات زمانی نظرات را در نظر می‌گیرد. برای انتخاب گره‌های دانه، الگوریتم ژنتیک حریصانه به‌کار گرفته شده است. پس از تعیین گره اولیه یا دانه، مدل پویایی نظرات فعال‌شده آغاز می‌شود. در این مرحله، گره اولیه و همسایگان آن براساس مدل انتشار آستانه خطی فعال‌سازی می‌شوند. سپس با بهره‌گیری از یادگیری Q بدون حالت، نظرات گره‌ها به‌روزرسانی می‌گردند. این فرآیند به صورت تکرارشونده ادامه می‌یابد تا زمانی که شرایط خاتمه مورد نظر برآورده شوند. نتایج تجربی حاصل از آزمایش بر روی چهار مجموعه‌داده‌ی شبکه‌های اجتماعی امضاشده نشان می‌دهد که روش پیشنهادی در مقایسه با بهترین روش‌های موجود، در معیار تعداد گره‌های فعال 14 درصد و در معیار میانگین نظرات مثبت 27 درصد بهبود عملکرد داشته است.

کلیدواژه‌ها

تحلیل شبکه‌های اجتماعی برخط

حداکثرسازی پویایی نظرات

گره شاخص

الگوریتم ژنتیک حریصانه

یادگیری Q

موضوعات

دفاع سایبری

عنوان مقاله English

An Improved Method for Dynamic Opinion Maximization Based on Greedy Genetic Algorithms

نویسندگان English

Hossein Rayatparvar ¹

Mohammad Hasani Ahangar ²

Aboulfazl Sarkardei ³

¹ Master's degree, Imam Hossein University, Tehran, Iran

² Professor, Imam Hossein University, Tehran, Iran

³ PhD student, Imam Hossein University (AS), Tehran, Iran

چکیده English

The analysis of online social network data represents a significant scientific challenge in contemporary research. Within the domain of user opinion analysis in these networks, dynamic opinion maximization has emerged as a nascent field of study. Prior investigations in this area have predominantly operated under the assumption of static, unchanging node opinions. Furthermore, the temporal evolution of user perspectives has received comparatively limited attention. This paper proposes a method for the dynamic opinion maximization problem, based on a greedy genetic algorithm, explicitly considering the dynamics of opinions and their evolution over time.The proposed method comprises two principal components: an activated opinion dynamics model and a seed node selection process. The activated opinion dynamics model is constructed by integrating the linear threshold model with stateless Q-learning, thereby explicitly accommodating the temporal fluctuations in opinions. A greedy genetic algorithm is employed for the selection of seed nodes. Following the identification of an initial seed node, the activated opinion dynamics model is initiated. During this phase, the seed node and its immediate neighbors are activated according to the linear threshold model. Subsequently, stateless Q-learning is utilized to update the opinions of the nodes. This iterative process continues until predefined termination criteria are satisfied. Experimental results on four signed social network datasets demonstrate that the proposed framework outperforms the state-of-the-art method by 14% in terms of the number of activated nodes and 27% in terms of average positive opinions.

کلیدواژه‌ها English

Online social network analysis

dynamic opinion maximization

seed nodes

genetic algorithm

Q-learning

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