Improving Link Prediction in Social Network with Population-Based Metaheuristics Algorithm
سال انتشار: 1393
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 779
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شناسه ملی سند علمی:
JR_IJMEC-4-12_024
تاریخ نمایه سازی: 16 فروردین 1395
چکیده مقاله:
Link prediction is a new interdisciplinary research direction in social network analysis (SNA) which, existing links are analyzed and future links are predicted among millions of users of social network. There are various prediction models including k-nearest neighbor (kNN), fuzzy inference, SVMs, Bayesian model, Markov model and others. In this paper we use Bayesian model to predict future links in flickr social network dataset, it was includes more than 35,000 users. then we use population-based metaheuristics algorithms to enhance accuracy of Bayesian Network Classifiers in feature Selection. We use two standard metric such as AUC and MAP measures for quantifying the accuracy of prediction algorithms.
کلیدواژه ها:
نویسندگان
Tarnaz chamani
Mazandaran university of science and technology, Babol, Iran
Alireza pourebrahimi
Islamic azad university, Tehran, Iran
Babak shirazi
Mazandaran university of science and technology, Babol, Iran