Paper Info
Title
Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features.
Abstract
In signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer AdaBoost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time.
Year
DOI
Venue
2015
10.1016/j.procs.2015.08.135
Procedia Computer Science
Keywords
Field
DocType
Link Prediction,Signed Social Network,AdaBoost Algorithm
Adaboost algorithm,Data mining,AdaBoost,Social network,Radial basis function kernel,Computer science,Transfer of learning,Support vector machine,Boosting (machine learning),Artificial intelligence,Classifier (linguistics),Machine learning
Conference
Volume
ISSN
Citations 
60
1877-0509
2
PageRank 
References 
Authors
0.35
17
4
Name
Order
Citations
PageRank
Anh-Thu Nguyen-Thi120.35
Phuc Quang Nguyen220.35
Thanh Duc Ngo38222.24
Tu-Anh Nguyen-Hoang461.08