NDLI: Time-Aware Ranking in Dynamic Citation Networks

Content Provider	IEEE Xplore Digital Library
Author	Ghosh, R. Tsung-Ting Kuo Chun-Nan Hsu Shou-De Lin Lerman, K.
Copyright Year	2011
Abstract	Many algorithms have been developed to identify important nodes in a complex network, including various centrality metrics and Page Rank, but most fail to consider the dynamic nature of the network. They therefore suffer from recency bias and fail to recognize important new nodes that have not had as much time to accumulate links as their older counterparts. This paper describes the Effective Contagion Matrix (ECM), a solution to address recency bias in the analysis of dynamic complex networks. The idea of ECM is to explicitly consider the temporal order of links and chains of links connecting to a node with some temporal decay factors. We tested ECM with three large real world citation networks on the task of predicting papers' future importance. We compared ECM's performance with two static metrics, degree-centrality and Page Rank, and two time-aware metrics, age-based Page Rank and Cite Rank. We show that ECM is more appropriate for predicting future citations and Page Rank scores with regard to new citations. We also describe a procedure to estimate ECM's parameters from the data. Combining all five scores into a v-SVR regression model of future citations improves the predictive performance further.
Starting Page	373
Ending Page	380
File Size	297266
Page Count	8
File Format	PDF
ISBN	9781467300056
DOI	10.1109/ICDMW.2011.183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Correlation Heuristic algorithms Random access memory Web pages data mining Complex networks dynamic networks citation networks network analysis Electronic countermeasures
Content Type	Text
Resource Type	Article

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