Identifying modules in complex networks by a graph-theoretical method and its application in protein interaction networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4682 LNAI, Issue , 2007, Pages 1090-1101

  Wang, Rui Sheng ^a   Zhang, Shihua ^c,d   Zhang, Xiang Sun ^c   Chen, Luonan ^b,e  

^a RENMIN UNIVERSITY OF CHINA   (China)

^b OSAKA SANGYO UNIVERSITY   (Japan)

^c CHINESE ACADEMY OF SCIENCES   (China)

^d UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^e SHANGHAI UNIVERSITY   (China)

Author keywords

Complex network; Functional module; Minimum spanning tree (MST); Protein interaction network

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; POLYNOMIALS; PROTEINS; TREES (MATHEMATICS);

COMPLEX NETWORKS; FUNCTIONAL MODULE; MINIMUM SPANNING TREE (MST); PROTEIN INTERACTION NETWORKS;

LARGE SCALE SYSTEMS;

EID: 38049058813     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74205-0_113     Document Type: Conference Paper

References (30)

1. Gibson, D., Kleinberg, J., Raghavan, P.: Inferring Web Communities from Link Topology. In Proceedings of the 9th ACM Conference on Hypertext and Hypermedia, (1998), NY. ACM Press
2. Gavin, A.C., Aloyand, P., Grandi, P., et al.: Proteome Survey Reveals Modularity of the Yeast Cell Machinery. Nature 440(2006) 631-636
3. Girvan, M., Newman, M.E.J.: Community Structure in Social and Biological Networks. Proc. Natl. Acad. Sci. USA, 99(2002) 7821-7826
4. Chen, L., Wu, L-Y.. Wang, Y., Zhang, X-S.: Inferring Protein Interactions from Experimental Data by Association Probabilistic Method. Proteins, 62(2006) 833-837
5. Zhang, S., Ning, X.-M., Zhang, X.-S.: Identification of Functional Modules in a PPI Network by Clique Percolation Clustering. Computational Biology and Chemistry, 30(2006) 445-451
6. Zhang, S., Liu, H.-W., Ning, X.-M., Zhang, X.-S.: A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks. Proceeding of Sixth IEEE International Conference on Data Mining-Workshops (ICDMW'06), (2006) 130-135
7. Wang, R., Zhou. T., Jing, Z., Chen, L.: Modelling Periodic Oscillation of Biological Systems with Multiple Time Scale Networks. Systems Biology, 1(2004) 71-84
8. Wang, Y., Joshi.T.. Xu, D., Zhang, X-S., Chen, L.: Inferring Gene Regulatory Networks from Multiple Microarray Datasets. Bioinformatics, 22(2006), 2413-2420
9. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. (1994), Cambridge University Press
10. White, S., Smyth, P.: A Spectral Clustering Approach to Finding Communities in Graphs. SIAM International Conference on Data Mining, (2005)
11. Danon, L., Daz-Guilera, A., Duch, J., Arenas, A.: Comparing Community Structure Identification. J. Statist. Mech.: Theory and Experiment, 09(2005), P09008
12. Angelini, L., Boccaletti, S., Marinazzo, D., et al.: Fast Identification of Network Modules by Optimization of Ratio Association. (2006), cond-mat/0610182
13. Reichardt, J., Bornholdt,S.: Detecting Fuzzy Community Structures in Somplex Networks with a Potts Model. Physical Review Letters, 93(2004), 218701
14. Paliai, G., Derenyi, I., Farkasl, I., et al.: Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature, 435(2005) 814-818
15. Vicsek, T.: Phase Transitions and Overlapping Modules in Complex Networks. Physica A, 378(2007) 20-32
16. Newman,M.E.J., Girvan,M.: Finding and Evaluating Community Structure in Networks. Phys. Rev. E 69(2004) 026113
17. Duch, J., Arenas,A.: Community Identification Using Extremal Optimization. Phys. Rev. E, 72(2005) 027104
18. Newman, M.E.J.: Modularity and Community Structure in Networks. Proc. Natl Acad. Sci. USA 103(23)(2006) 8577-8582
19. Guimer, R., Amaral, L.A.N. : Functional Cartography of Complex Metabolic Networks. Nature, 438(2005) 895-900
20. Fortunato, S., Barthélémy, M.: Resolution Limit in Community Detection. Proc. Natl. Acad. Sci. USA, 104(2007) 36-41
21. Gustafsson, M., Hö rnquista,M., Lombardi, A.: Comparison and Validation of Community Structures in Complex Networks. Physica A, 367(2006) 559-576
22. Newman, M.E.J.: Detecting Community Structure in Networks. Eur. Phys. J. B 38(2004) 321-330
23. Gower, J.C., Ross, G.J.S.: Minimum Spanning Trees and Single-Linkage Cluster Analysis. Applied Statistics, 18(1969) 54-64
24. Zahn, C.T.: Graph-Theoretical Methods for Detecting and Describing gestalt clusters. IEEE Transaction on Computers, C20(1971) 68-86
25. Pävinen, N.: Clustering with a Minimum Spanning Tree of Scale-free Like Structure. Pattern Recognition Letters, 26(2005) 921-930
26. Varma, S., Simon, R.: Iterative Class Discovery and Feature Selection Using Minimal Spanning Trees. BMC Bioinformatics, 5(2004) 126-134
27. Xu, Y., Olman, V., Xu, D.: Clustering Gene Expression Data Using a GraphTheoretic Approach: an Application of Minimum Spanning Trees. Bioinformatics, 18(2002) 536-45
28. Xu, Y., Olman, V., and Uberbacher, E.C.: A Segmentation Algorithm for Noisy Images: Design and Evaluation. Pattern Recognition Letters, 19(1998) 1213-1224
29. Mewes, H.W., Frishman, D., Guidener, U. et al.: MIPS: a Database for Genomes and Protein Sequences. Nucleic Acids Res, 30(2002) 31-34
30. Vathy-Fogarassy, A., Feil, B., Abonyi, J.: Minimal Spanning Tree Based Fuzzy Clustering. ENFORMATIKA Transactions on Engineering, Computing and Technology, 8(2005) 7-12