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CLC number: TP393
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-08-25
Cited: 2
Clicked: 7075
Time-series prediction based on global fuzzy measure in social networks
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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.
@article{title="Time-series prediction based on global fuzzy measure in social networks",
author="Li-ming Yang, Wei Zhang, Yun-fang Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="10",
pages="805-816",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500025"
}
%0 Journal Article
%T Time-series prediction based on global fuzzy measure in social networks
%A Li-ming Yang
%A Wei Zhang
%A Yun-fang Chen
%J Frontiers of Information Technology & Electronic Engineering
%V 16
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%P 805-816
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500025
TY - JOUR
T1 - Time-series prediction based on global fuzzy measure in social networks
A1 - Li-ming Yang
A1 - Wei Zhang
A1 - Yun-fang Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 10
SP - 805
EP - 816
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500025
Abstract: 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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