Full Text:   <2225>

Summary:  <1745>

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: 6410

Citations:  Bibtex RefMan EndNote GB/T7714


Yun-fang Chen


-   Go to

Article info.
Open peer comments

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

Share this article to: More |Next Article >>>

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",
publisher="Zhejiang University Press & Springer",

%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
%N 10
%P 805-816
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500025

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
ER -
DOI - 10.1631/FITEE.1500025

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Araujo, E., 2008. Social relationship explained by fuzzy logic. Proc. IEEE Int. Conf. on Fuzzy Systems, p.2129-2134.

[2]Bastani, S., Jafarabad, A.K., Zarandi, M.H.F., 2013. Fuzzy models for link prediction in social networks. Int. J. Intell. Syst., 28(8):768-786.

[3]Brunelli, M., Fedrizzi, M., 2009. A fuzzy approach to social network analysis. Proc. Int. Conf. on Advances in Social Network Analysis and Mining, p.225-230.

[4]Brunelli, M., Fedrizzi, M., Fedrizzi, M., 2014. Fuzzy $m$-ary adjacency relations in social network analysis: optimization and consensus evaluation. Inform. Fusion, 17:36-45.

[5]de Sa, H.R., Prudencio, R.B.C., 2011. Supervised link prediction in weighted networks. Proc. Int. Conf. on Neural Networks, p.2281-2288.

[6]Ebel, H., Davidsen, J., Bornholdt, S., 2002. Dynamics of social networks. Complexity, 8(2):24-27.

[7]Freeman, L.C., 1978. Centrality in social networks conceptual clarification. Soc. Netw., 1(3):215-239.

[8]Freeman, L.C., 2004. The Development of Social Network Analysis: a Study in the Sociology of Science. Empirical Press, Vancouver.

[9]Hasan, M.A., Chaoji, V., Salem, S., et al., 2006. Link prediction using supervised learning. Proc. SDM Workshop on Link Analysis, Counter-Terrorism and Security, p.1-10.

[10]He, Y.L., Liu, J.N.K., Hu, Y.X., et al., 2015. OWA operator based link prediction ensemble for social network. Expert Syst. Appl., 42(1):21-50.

[11]Huang, Z., Lin, D.K.J., 2009. The time-series link prediction problem with applications in communication surveillance. INFORMS J. Comput., 21(2):286-303.

[12]Jaccard, P., 1901. Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull. Soc. Vaud. Sci. Nat., 37:547-579 (in French).

[13]Jin, E.M., Girvan, M., Newman, M.E.J., 2001. The structure of growing social networks. Available from http://ideas.repec.org/p/wop/safiwp/01-06-032.html [Accessed on June 30, 2015].

[14]Khorasani, E.S., Rahimi, S., Patel, P., et al., 2011. CWJess: an expert system shell for computing with words. Proc. IEEE Int. Conf. on Information Reuse and Integration, p.396-399.

[15]Nair, P.S., Sarasamma, S.T., 2007. Data mining through fuzzy social network analysis. Proc. 26th Annual Meeting of the North American Fuzzy Information Processing Society, p.251-255.

[16]Ryoke, M., Nakamori, Y., Suzuki, K., 1995. Adaptive fuzzy clustering and fuzzy prediction models. Proc. Int. Joint Conf. of 4th IEEE Int. Conf. on Fuzzy Systems and 2nd Int. Fuzzy Engineering Symp., p.2215-2220.

[17]Yan, B., Gregory, S., 2011. Finding missing edges and communities in incomplete networks. J. Phys. A, 44:495102.1-495102.15.

[18]Zadeh, L.A., 1965. Fuzzy sets. Inform. Contr., 8(3):338-353.

[19]Zhang, J.Y., Borland, R., Coghill, K., 2011. Evaluating the effect of health warnings in influencing Australian smokers’ psychosocial and quitting behaviours using fuzzy causal network. Expert Syst. Appl., 38(6):6430-6438.

[20]Zhu, J., Xie, Q., Chin, E.J., 2012. A hybrid time-series link prediction framework for large social network. Proc. 23rd Int. Conf. on Database and Expert Systems Applications, p.345-359.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE