روشی نوین برای پیش‌بینی ارتباط در شبکه‌های اجتماعی ناهمگن
محورهای موضوعی : مهندسی برق و کامپیوترسعیده رضاوندی شعاعی 1 , هادی زارع 2
1 - دانشگاه تهران
2 - دانشگاه تهران
کلید واژه: اندازه شباهت پیش‌بینی ارتباط شبکه‌های اجتماعی شبکه‌های اجتماعی ناهمگن کاوش ارتباطات یادگیری با ناظر,
چکیده مقاله :
با گسترش روزافزون شبکه‌های اجتماعی، علوم شبکه مورد توجه بسیاری از پژوهشگران در زمینه‌های مختلف قرار گرفته است. علاوه بر آن بسیاری از مسایل کاربردی مهندسی با استفاده از ابزار شبکه‌های اجتماعی مدل‌سازی شده‌اند. پیش‌بینی تغییر و تحول در ساختار شبکه‌های اجتماعی یکی از مسایل اساسی در تحلیل شبکه‌های اجتماعی است که با عنوان مسأله پیش‌بینی ارتباط در علوم شبکه شناخته می‌شود. امروزه با گسترش استفاده از شبکه‌های اجتماعی، فعالیت افراد در قالب چندین شبکه با عنوان شبکه‌های اجتماعی ناهمگن رواج پیدا کرده است. پیش‌بینی ارتباط در شبکه‌های اجتماعی ناهمگن را می‌توان بر اساس اطلاعات اضافی موجود نسبت به روش‌های قبلی مورد بهبود قرار داد. در رویکرد پیشنهادی این مقاله، ابتدا یک معیار شباهت جدید برای کاربران در شبکه‌های ناهمگن بر اساس توسعه روش‌های مطرح پیشین و با در نظر گرفتن ارتباط بین لایه‌های مختلف معرفی می‌شود، سپس با استفاده از رویکرد یادگیری باناظر و بهره‌گیری از ویژگی‌های تولیدشده بر مبنای معیار شباهت معرفی‌شده، الگوریتم پیشنهادی مورد تشریح قرار می‌گیرد. برای ارزیابی روش پیشنهادی از معیارهای استاندارد همانند دقت و صحت بهره گرفته‌ایم. مقایسه روش پیشنهادی با روش‌های شناخته‌شده پیشین بر روی مجموعه داده‌های مختلف نشان می‌دهد که روش پیشنهادی ما برای پیش‌بینی ارتباط از عملکرد بهتر و مطلوب‌تری برخوردار است به طوری که از نظر صحت تا ۲۰ درصد موجب بهبود عملکرد شده است.
Nowadays the network science has been attracted many researchers from a wide variety of different fields and many problems in engineering domains are modelled through social networks measures. One of the most important problems in social networks is the prediction of evolution and structural behavior of the networks that is known as link prediction problem in the related literature. Nowadays people use multiple and different social networks simultaneously and it causes to demonstrate a new domain of research known as heterogenous social networks. There exist a few works on link prediction problem on heterogenous networks. In this paper, first a novel similarity measure for users in heterogenous networks is defined. Then a novel link prediction algorithm is described through a supervised learning approach which is consisted by the generated features from the introduced similarity measures. We employ the standard evaluation criteria for verification of the proposed approach. The comparison of the proposed algorithm to the other well-known earlier works showed that our proposed method has better performance than the other methods based on testing on several network datasets.
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