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 …

Date

December 11, 2011

Authors

Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, Kristina Lerman

Conference

2011 ieee 11th international conference on data mining workshops

Pages

373-380

Publisher

IEEE