1. Lin, T.Y.: Granular Computing: Fuzzy Logic and Rough Sets. In: Zadeh, L.A., Kacprzyk, J. (eds.) Computing with words in information- intelligent systems, pp. 183–200. Physica-Verlag, Heidelberg (1999)

   Google Scholar 

2. Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 19, 111–127 (1997)

   Article MathSciNet  Google Scholar 

3. Bargiela, A., Pedrycz, W.: Human centric information processing through granular modelling. Springer, Berlin (2009)

   Book  Google Scholar 

4. Yao, Y.Y.: Triarchic theory of granular computing. In: Zhang, Y.P., et al. (eds.), pp. 115–143. Science press, Beijing (2010) (in Chinese)

   Google Scholar 

5. Pawlak, Z.: Rough Sets Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

   MATH  Google Scholar 

6. Zhang, L., Zhang, B.: Theory and Applications of Problem Solving– the Quotient Space granular computation theory and Applications (the second version). Tsinghua University Press, Beijing (2007) (in Chinese)

   Google Scholar 

7. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. on Sys. Sci. and Cyber. 4(2), 100–107 (1968)

   Article  Google Scholar 

8. Goldberg, A.V., Harrelson, C.: Computing the shortest path: A* search meets graph theory. In: 16th ACM-SIAM symposium on discrete algorithms, pp. 156–165 (2005)

   Google Scholar 

9. Goldberg, A.V., Werneck, R.F.: Computing point-to-point shortest paths from external memory. In: Workshop on Algorithm Engineering and Experiments, pp. 26–40 (2005)

   Google Scholar 

10. Goldberg, A.V., Kaplan, H., Werneck, R.F.: Better landmarks within reach. In: Demetrescu, C. (ed.) WEA 2007. LNCS, vol. 4525, pp. 38–51. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

11. Sanders, P., Schultes, D.: Engineering Highway Hierarchies. In: Azar, Y., Erlebach, T. (eds.) ESA 2006. LNCS, vol. 4168, pp. 804–816. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

12. Bauer, R., Delling, D.: SHARC:Fast and robust unidirectional routing. In: Workshop on Algorithm Engineering and Experiments (2008)

    Google Scholar 

13. Zhang, L., He, F.G., Zhang, Y.P., et al.: A new algorithm for optimal path finding in complex networks based on the quotient space. Fundamenta Informaticae 93(4), 459–469 (2009)

    MATH MathSciNet  Google Scholar 

14. Pothen, A., Simon, H., Liou, K.P.: Partitioning sparse matrices with eigenvectors of Graphs. SIAMJ. Matrix Anal. Appl. 11(3), 430–452 (1990)

    Article MATH MathSciNet  Google Scholar 

15. Girvan, M., Newman, M.E.J.: Community structure in social and biological network. Proc. Natl. Acad. Sci. 99, 7821–7826 (2001)

    Article MathSciNet  Google Scholar 

16. Radicchi, F., Castellano, C., Cecconi, F., et al.: Defining and identifying communities in networks. Eur. Phys. J. B. 38, 373–380 (2004)

    Article  Google Scholar 

17. Fortunato, S., Latora, V., Marchiori, M.: A method for finding community structure based on information centrality. Phys. Rev. E 70, 056104 (2004)

    Google Scholar 

18. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)

    Google Scholar 

19. Ulrik, B., Daniel, D., Marco, G., et al.: On Modularity Clustering. IEEE Transactions on In Knowlegde Data Engineering 20(2), 172–188 (2007)

    Google Scholar 

20. Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proc. of the National Academy of Sciences of the United States of America 104(1), 36–41 (2007)

    Article  Google Scholar 

21. Zhuge, H., Zhang, J.S.: Topological centrality and its applications (2009),http://arxiv.org/abs/0902.1911v1

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