DocumentCode :
169299
Title :
Link prediction based on time-varied weight in co-authorship network
Author :
Shiping Huang ; Yong Tang ; Feiyi Tang ; Jianguo Li
Author_Institution :
Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China
fYear :
2014
fDate :
21-23 May 2014
Firstpage :
706
Lastpage :
709
Abstract :
Social networks are very dynamic objects, since new edges and vertices are added to the graph over the time. Link prediction is an important task in social network analysis and is useful in many application domains. In the recent years, there is significant interest in methods that represent the social network in the form of a graph and leverage topological and semantic measures of similarity between two nodes to make predictions. In this article, we propose a hybrid approach utilizing time-varied weight information of links. We focus on the problem of link prediction particularly in the context of evolving co-authorship. Experiments have shown that the link prediction algorithm based on time-varied weight can reach better result.
Keywords :
graph theory; social networking (online); co-authorship network; graph; link prediction; semantic measures; social network analysis; time-varied weight; topological measures; Algorithm design and analysis; Educational institutions; Measurement; Prediction algorithms; Predictive models; Probabilistic logic; Social network services; link prediction; social network; time-varied weight;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Supported Cooperative Work in Design (CSCWD), Proceedings of the 2014 IEEE 18th International Conference on
Conference_Location :
Hsinchu
Type :
conf
DOI :
10.1109/CSCWD.2014.6846931
Filename :
6846931
Link To Document :
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