DocumentCode :
3576287
Title :
Effective Social Circle Prediction Based on Bayesian Network
Author :
Yan Tang ; Lili Lin ; Zhuoming Xu ; Yu Wang
Author_Institution :
Coll. of Comput. & Inf., Hohai Univ., Nanjing, China
fYear :
2014
Firstpage :
131
Lastpage :
135
Abstract :
User´s personal social networks are big and cluttered, yet contain highly valuable information. Organizing users´ friends into circles or communities is a fundamental task in social network research. Social network sites allow users to manually categorize their friends into social circles, however this process is laborious and inadaptable to changes. In this paper, we study novel ways of automatically determining users´ social circles. We treat this task as a classification problem on a user´s ego-network, a network of connections between friends. Based on Bayesian Network (BN), we develop a model for determining whether a query user Uq is in main user Um´s social circle. First, we transform the original social network data to make it suitable for BN modeling, and build an Initial Bayesian Network (IBN) of Um using the state-of-the-art BN learning algorithm. Then, we propose a new method to improve the IBN by adding important parents to the class variable. Lastly, leveraging carefully designed threshold, we use the final BN to determine the existence of Uq in the social circle of Um. Modeling social circle with BN allows us to quantify user´s social circle existence with probability and run query with missing values/evidences. Using ground-truth data from Facebook and Twitter, experimental results indicate that our BN model could accurately determine user´s existence in social circle and outperforms four baseline predictors, namely Naïve Bayes, IBL, OneR and J48, showing promising application potential in the social circle research area.
Keywords :
belief networks; classification; learning (artificial intelligence); probability; query processing; social networking (online); BN learning algorithm; BN modeling; Facebook; IBN; Twitter; categorization; classification problem; initial Bayesian network; probability; query user; run query; social circle prediction; social network data; social network sites; user ego-network; user personal social networks; Accuracy; Bayes methods; Data models; Facebook; Predictive models; Twitter; Bayesian network; feature selection; social circle prediction; social network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Information System and Application Conference (WISA), 2014 11th
Print_ISBN :
978-1-4799-5726-2
Type :
conf
DOI :
10.1109/WISA.2014.32
Filename :
7058001
Link To Document :
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