NDLI: Link prediction based on time-varied weight in co-authorship network

Content Provider	IEEE Xplore Digital Library
Author	Shiping Huang Yong Tang Feiyi Tang Jianguo Li
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci., South China Normal Univ., Guangzhou, China (Yong Tang; Jianguo Li) \|\| Coll. of Eng. & Sci., Victoria Univ., Melbourne, VIC, Australia (Feiyi Tang) \|\| Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China (Shiping Huang)
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.
Sponsorship	IEEE SMC Soc.
Starting Page	706
Ending Page	709
File Size	291770
Page Count	4
File Format	PDF
ISBN	9781479937769
DOI	10.1109/CSCWD.2014.6846931
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prediction algorithms Social network services Educational institutions Measurement Algorithm design and analysis Probabilistic logic Predictive models social network link prediction time-varied weight
Content Type	Text
Resource Type	Article

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