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CLC number: TP393

On-line Access: 2015-10-08

Received: 2015-01-18

Revision Accepted: 2015-07-26

Crosschecked: 2015-08-25

Cited: 2

Clicked: 1735

Citations:  Bibtex RefMan EndNote GB/T7714


Yun-fang Chen


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.10 P.805-816


Time-series prediction based on global fuzzy measure in social networks

Author(s):  Li-ming Yang, Wei Zhang, Yun-fang Chen

Affiliation(s):  Department of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Corresponding email(s):   chenyf@njupt.edu.cn

Key Words:  Time-series network, Fuzzy network, Fuzzy Markov chain

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Li-ming Yang, Wei Zhang, Yun-fang Chen. Time-series prediction based on global fuzzy measure in social networks[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(10): 805-816.

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Social network analysis (SNA) is among the hottest topics of current research. Most measurements of SNA methods are certainty oriented, while in reality, the uncertainties in relationships are widely spread to be overridden. In this paper, fuzzy concept is introduced to model the uncertainty, and a similarity metric is used to build a fuzzy relation model among individuals in the social network. The traditional social network is transformed into a fuzzy network by replacing the traditional relations with fuzzy relation and calculating the global fuzzy measure such as network density and centralization. Finally, the trend of fuzzy network evolution is analyzed and predicted with a fuzzy Markov chain. Experimental results demonstrate that the fuzzy network has more superiority than the traditional network in describing the network evolution process.

The topic, as the authors asserted in the paper, is interesting and relevant. The authors did a good presentation of the mathematical model, which seems solid enough. Meanwhile, the experimental results illustrated that the adopted fuzzy network has more superiority than traditional network in describing the network evolution process.




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