پیش بینی پیوند مبتنی بر اعتماد با استفاده از مدل محاسبات فازی در شبکه های اجتماعی
محورهای موضوعی : هوش مصنوعی و رباتیک
فاطمه حسین خانی
1
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علی هارون آبادی
2
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سعید ستایشی
3
1 - گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران
2 - استادیار، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
3 - دانشگاه صنعتی امیر کبیر
کلید واژه: پیش بینی پیوند, شبکه های اجتماعی علامت دار, اعتماد, عدم اعتماد, محاسبات فازی ,
چکیده مقاله :
پیش بینی پیوند امری ضروری برای بررسی پیوند بین گره ها در شبکه های اجتماعی است. گسترش و مدل سازی شبکه های اجتماعی منجر به پیدایش شبکه های اجتماعی به صورت علامت دار، جهت دار و وزنی می شود. روابط کاربران در شبکه های اجتماعی علامت دار جنبه های ذهنی و نامتقارن وابسته به این حوزه را تعریف می کنند، لذا هر دو اصطلاح اعتماد و عدم اعتماد چالش برانگیز هستند. برای حل مسئله پراکندگی در شبکه ها و غلبه بر ابهام در روابط، یک روش اعتماد-عدم اعتماد مبتنی بر محاسبات فازی برای محاسبه قدرت پیوندها پیشنهاد می شود. هدف روش پیشنهادی پیش بینی پیوند برای حل مسئله پراکندگی در شبکه های اجتماعی علامت دار با ترکیب ویژگی های توصیف کاربران در شبکه های اجتماعی است که با تاثیر مستقیم گره های برتر و تاثیر غیرمستقیم گره های معمولی بر پیش‌بینی رتبه‌بندی ها ارزیابی می شود. اعتماد با یک سیستم فازی ممدانی مبتنی بر ویژگی های انعکاس شباهت فازی، اعتماد کلی و عدم اعتماد کلی تعیین می شود. ارزیابی روش پیشنهادی با معیار دقت بر روی مجموعه داده های شبکه های اجتماعی Epinions وSlashdot انجام شد. دقت روش پیشنهادی در مجموعه داده هایEpinions و Slashdotبه ترتیب برابر 0.991 و 0.998 می باشد. نتایج به دست آمده نشان می دهد روش پیشنهادی نسبت به مشکل پراکندگی داده ها در شبکه های اجتماعی علامت دار قوی عمل می-کند و این اثربخشی مدل پیشنهادی را بیان می نماید.
Link prediction is an important to check link between nodes in social networks. The modeling of social networks leads to emergence of signed, directed and weighted social networks. The relationships of users in social networks are characterized by subjective, asymmetric and ambiguous aspects related to this domain, then both terms of trust and distrust are challenging. To solve the problem of sparsity in networks and overcome ambiguity in relationships, a trust-distrust method based on fuzzy computational is proposed to calculate strength of links. The purpose of proposed link prediction is to solve problem of sparsity in signed social networks by combining descriptive features of users with the direct influence of top nodes and the indirect influence of common nodes on rating prediction. Trust is determined by a Mamdani fuzzy system based on mirroring of similarity fuzzy features, overall trust and overall distrust. The evaluation of the proposed method was done with the accuracy measure on datasets of Epinions and Slashdot. The accuracy of proposed method in Epinions and Slashdot datasets is 0.991 and 0.998, respectively. The obtained results show that proposed method works well for problem of data sparsity in signed social networks and show the effectiveness of proposed model.
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