ارائه روشی جهت شبکه‌های اجتماعی چند لایه‌ای پویا جهت کشف گروه‌های تاثیرگذار مبتنی بر ترکیبب الگوریتم تکاملی جهش قورباغه و خوشه‌بندی C-means
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندلیدا ندرلو 1 , محمد تحقیقی شربیان 2
1 - دانشگاه روزبه- دانشکده کامپیوتر- زنجان- ایران
2 - استادیار، دانشکده مهندسی برق و کامپیوتر، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران
کلید واژه: گروه‌های تاثیرگذار, الگوریتم تکاملی جهش قورباغه و خوشه‌بندی C-means, شبکه‌های اجتماعی چند لایه‌ای پویا,
چکیده مقاله :
امروزه علم و فن آوری با آهنگی شتابناک در حال رشد است و شبکه های اجتماعی پیچیده به بخشی ضروری از زندگی تبدیل شده اند، آن گونه که بحث جدایی مردم از شبکه های درهم پیچیده ای که مبتنی بر نیازهای اساسی زندگی است بحث ناگزیری در زندگی روزمره و عرصه دانش است. در پژوهش پیش رو مدلی برای شبکه های اجتماعی چندلایه ای پویا برای کشف گروه های تأثیرگذار، مبتنی بر ترکیبب الگوریتم تکاملی جهش قورباغه و خوشه بندی C-means ارائه شده است. بدین ترتیب که پس از جمع‌آوری داده‌ها به پاک سازی و نرمال سازی آن ها پرداخته شد تا داده‌های مطلوب منجر به شناسایی افراد و گروه‌های مؤثر شود که در ادامه کار ماتریس تصمیم شکل گرفت و از روی آن شناسایی و خوشه‌بندی(مبتنی بر خوشه‌بندی فازی) انجام شد و اهمیت گروه‌ها نیز مشخص گردید. برای دستیابی به افراد و گروه‌های تأثیرگذار در شبکه‌های اجتماعی، از الگوریتم قورباغه جهنده برای بهبود تشخیص پارامترهای تأثیرگذار استفاده شد که باعث بهبود اهمیت گره ها شده است. در ارزیابی و شبیه سازی بخش خوشه‌بندی، روش پیشنهادی با روش K-means مقایسه و نتیجه مقدار تعادل روش در انتخاب خوشه برابر 5 شد. گفتنی است که روش پیشنهادی به نسبت روشهای مورد مقایسه، بهبود مناسب تری را نشان داد. همچنین ارزیابی معیار صحت روش پیشنهادی به نسبت روشهای همسان بهبود 3.3 داشته و نسبت به روش پایه M-ALCD بهبود 3.8 را به ثبت رسانده است.
Introduction: The current research examines a more complex social network called a multi-layered social network. Recently, the concept of the multilayer network has emerged from the area of complex networks, under the domain of complex systems. In the field of big data, simple and multi-layered social networks can be found everywhere and in all fields. The estimation of the importance of each node in these two types of networks is not the same, and weighting the nodes is very necessary to control the network. For this purpose, the relationship between the characteristics of the nodes and the relationship with the network structure should be examined. To find the degree of each node in the system function, parameters like reliability, controllability, and power should be considered. In this paper, a model for dynamic multi-layer social networks to discover influential groups, based on the combination of evolutionary frog jump algorithm and C-means clustering, has been presented.Method: Once data are collected, they were cleaned and normalized so that the desired data leads to the identification of effective individuals and groups. The decision matrix is constructed based on these data. Using this matrix, identification, and clustering (based on fuzzy clustering) were conducted, and the importance of the groups was also determined to determine influential people and groups in social networks. The Jumping frog algorithm was used to improve the detection of influential parameters.Results: In the evaluation and simulation of the clustering part, the proposed method was compared with the K-means method and the balance value of the method in cluster selection was 5. It should be noted that the proposed method showed better improvement compared to the compared methods. Also, the evaluation of the accuracy criterion of the proposed method has improved by 3.3 compared to similar methods and recorded an improvement of 3.3 compared to the basic M-ALCD method.Discussion: In this paper, a multi-layer local community detection model is proposed, which is based on structure and feature information. This model can use the information of the characteristics of the nodes and the information of the strength of similarity that is revealed by social exchanges and improves the accuracy of community detection in Improve multilayer networks. Due to its modularity and computational efficiency, this algorithm performs better on most data sets, unlike the classic multi-layer and global community detection algorithms
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