ارائه یک معیار مشابهت جهت پیش‌بینی پیوند در شبکههای اجتماعی
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
علی سرآبادانی
1
(
دانشجوی دکتری، مهندسی فناوری اطلاعات، دانشگاه قم، قم، ایران
)
خیرالله رهسپارفرد
2
(
استادیار، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران
)
سید مرتضی پورنقی
3
(
استادیار، گروه رایانش امن، دانشکده رایانه، شبکه و ارتباطات، دانشگاه جامع امام‌حسین (ع)، تهران، ایران
)
کلید واژه: تحلیل شبکه اجتماعی, پیش‌بینی پیوند, معیار مشابهت, شبکه هم نویسندگی,
چکیده مقاله :
یک شبکه اجتماعی، ساختاری متشکل از افراد یا سازمان‌ها است. تحلیل شبکههای اجتماعی، مبتنی بر رویکردی است که در آن شبکه را به صورت مجموعه‌ای از گره‌ها و روابط میان آن‌ها درنظر می‌گیرد. گره‌ها شامل اشخاص و موجودیت‌های درون شبکه هستند که با یکدیگر در تعامل ند و در واقع بازیگران درون شبکه محسوب می‌شوند که روابط میان آن‌ها به صورت اتصالاتی بین گره‌ها نمایش داده می‌شود. باتوجه به تعداد رو به رشد کاربران شبکههای اجتماعی، تحلیل روابط حاکم بر آن، پیش‌بینی پیوند‌ها و تعامل‌های ناشی از ارتباط میان گره‌ها (پیش‌بینی لینک یا پیوند، یعنی پیش‌بینی تعامل جدیدی که قرار است در آینده رخ دهد) از چالش‌های جدی در شبکههای اجتماعی می باشد. ما در این مقاله یک معیار شباهت جدید برای پیش‌بینی لینک در شبکههای اجتماعی را پیشنهاد می‌دهیم. این معیار را با چهار روش پیش‌بینی لینک Jaccard، Salton Index، Salton Cosine و Resource Allocation مقایسه می‌کنیم. ما شبیه‌سازی معیار پیشنهادی خود را بر روی پنج مجموعه داده در شبکههای اجتماعی، انجام می‌دهیم. نتایج شبیه‌سازی نشان می‌دهد که معیار پیشنهادی ما عملکرد بهتری نسبت به سایر روش‌های پیش‌بینی پیوند در شبکههای اجتماعی برروی همه دیتاست‌ها را دارد. تجزیه و تحلیل شبکه های اجتماعی به دلیل کاربرد گسترده آن در ثبت تعاملات اجتماعی اخیراً توجه بسیاری را در بین محققان به خود جلب کرده است. پیش‌بینی پیوند، مربوط به احتمال وجود پیوند بین دو گره شبکه که متصل نیستند، یک مشکل کلیدی در تحلیل شبکههای اجتماعی است. روش های زیادی برای حل مشکل پیشنهاد شده است. در میان این روش‌ها، روش‌های مبتنی بر شباهت با درنظرگرفتن ساختار شبکه و استفاده به عنوان معیاری اساسی از تعداد همسایه‌های مشترک بین دو گره برای ایجاد شباهت ساختاری، کارایی خوبی از خود نشان می‌دهند.
Introduction: A social network is a social structure made up of individuals or organizations. Social network analysis is an approach in which the network is considered as a set of nodes and relationships between them. Nodes are individuals and actually actors in the network and the relationships between them are displayed as connections between nodes.Method: Among many social network analysis issues, link prediction has attracted much attention due to the growing number of social network users. Link prediction means predicting which new interaction is going to happen in the future. Traditional link prediction methods considered pairs of nodes as a unit and made decisions based on commonalities between them. In addition, we proposed a new similarity measure for link prediction in social networks.Results: We compared this criterion with four prediction methods of Jaccard link, Salton Index, Salton Cosine, and resource allocation). Experimental runs in this article were carried out on five social network datasets. Our results showed that this criterion performed better than other link prediction techniques on all datasets.Discussion: Social network analysis has recently attracted lots of attention among researchers due to its wide applicability in capturing social interactions. Link prediction, related to the likelihood of having a link between two nodes of the network that are not connected, is a key problem in social network analysis. Many methods have been proposed to solve the problem. Among these methods, similarity-based methods exhibit good efficiency by considering the network structure and using as a fundamental criterion the number of common neighbors between two nodes to establish structural similarity.
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