Title :
A New Method for Link Prediction Using Various Features in Social Networks
Author :
Zhang Yu ; Gao Kening ; Li Feng ; Yu Ge
Author_Institution :
Comput. Center, Northeastern Univ., Shenyang, China
Abstract :
Link prediction is a basic problem in the research of social networks. At present, most link prediction algorithms are based on the features extracted from network structure, few research concerns the effect of natural attributes of nodes for creating a link. In this paper we develop a novel way to predict links based on Random Walk algorithm using the information from both the network topology and rich node attributes. The experiment result show that our method can help improves the prediction accuracy and it proves that node attributes have a real effect on link creation.
Keywords :
feature extraction; social networking (online); topology; features extraction; link prediction; network structure; network topology; random walk algorithm; social networks; Educational institutions; Feature extraction; Indexes; Network topology; Prediction algorithms; Social network services; Training; Random Walk; link prediction; node attribute; social network;
Conference_Titel :
Web Information System and Application Conference (WISA), 2014 11th
Print_ISBN :
978-1-4799-5726-2
DOI :
10.1109/WISA.2014.34