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Real-World Cases of Network Structure Analytics

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Computational Intelligence for Network Structure Analytics

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Abstract

In complex systems, except for the issues discussed in previous chapters, the issues, including recommender system, network alignment and influence maximization etc. are also NP-hard problems, and they can be modeled as optimization problems. Computational intelligence algorithms, especially evolutionary algorithms, have been successfully employed to these network structure analytics topics. In this chapter, we will present how to use computational intelligence techniques to tackle the recommendation system, the network alignment, and the influence maximization problem in complex networks. First, an evolutionary multiobjective algorithm is used for recommendation. And then, a memetic algorithm for influence maximization is introduced. Finally, a memetic algorithm for global biological network alignment is presented.

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References

  1. Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  3. Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422(6928), 198–207 (2003)

    Article  Google Scholar 

  4. Ahuja, R.K., Ergun, Ö., Orlin, J.B., Punnen, A.P.: A survey of very large-scale neighborhood search techniques. Discrete Appl. Math. 123(1), 75–102 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. Aladağ, A.E., Erten, C.: Spinal: scalable protein interaction network alignment. Bioinformatics 29(7), 917–924 (2013)

    Article  Google Scholar 

  6. Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)

    Article  Google Scholar 

  7. Blin, G., Sikora, F., Vialette, S.: Querying graphs in protein-protein interactions networks using feedback vertex set. IEEE/ACM Trans. Comput. Biol. Bioinformatics (TCBB) 7(4), 628–635 (2010)

    Article  Google Scholar 

  8. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory and Exp. 2008(10), P10,008 (2008)

    Google Scholar 

  9. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  10. Bogdanov, P., Singh, A.K.: Molecular function prediction using neighborhood features. IEEE/ACM Trans. Comput. Biol. Bioinformatics (TCBB) 7(2), 208–217 (2010)

    Article  Google Scholar 

  11. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998)

    Article  Google Scholar 

  12. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  13. Cardona, G., Rossello, F., Valiente, G.: Comparison of tree-child phylogenetic networks. IEEE/ACM Trans. Comput. Biol. Bioinformatics (TCBB) 6(4), 552–569 (2009)

    Article  Google Scholar 

  14. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 199–208. ACM (2009)

    Google Scholar 

  15. Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Trans. Evol. Comput. 15(5), 591–607 (2011)

    Article  Google Scholar 

  16. Chen, Y.C., Zhu, W.Y., Peng, W.C., Lee, W.C., Lee, S.Y.: CIM: community-based influence maximization in social networks. ACM Trans. Intell. Syst. Technol. 5(2), 25 (2014)

    Article  Google Scholar 

  17. Chindelevitch, L., Ma, C.Y., Liao, C.S., Berger, B.: Optimizing a global alignment of protein interaction networks. Bioinformatics 486 (2013)

    Google Scholar 

  18. Cicirello, V.A., Smith, S.F.: Modeling ga performance for control parameter optimization. In: GECCO, pp. 235–242 (2000)

    Google Scholar 

  19. Clark, C., Kalita, J.: A comparison of algorithms for the pairwise alignment of biological networks. Bioinformatics 30(16), 2351–2359 (2014)

    Article  Google Scholar 

  20. Clark, C., Kalita, J.: A multiobjective memetic algorithm for ppi network alignment. Bioinformatics (2015)

    Google Scholar 

  21. Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the Third Annual ACM Symposium on Theory of Computing, pp. 151–158. ACM (1971)

    Google Scholar 

  22. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  23. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, pp. 57–66. ACM (2001)

    Google Scholar 

  24. Fields, S.: Song, O.k.: A novel genetic system to detect protein-protein interactions. Nature 340, 245–246 (1989)

    Article  Google Scholar 

  25. Flannick, J., Novak, A., Srinivasan, B.S., McAdams, H.H., Batzoglou, S.: Graemlin: general and robust alignment of multiple large interaction networks. Genome Res. 16(9), 1169–1181 (2006)

    Article  Google Scholar 

  26. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  27. Gong, M., Cai, Q., Chen, X., Ma, L.: Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans. Evol. Comput. 18(1), 82–97 (2014)

    Article  Google Scholar 

  28. Goyal, A., Lu, W., Lakshmanan, L.V.: CELF++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th International Conference Companion on World Wide Web, Hyderabad, India, pp. 47–48. ACM (2011)

    Google Scholar 

  29. Hashemifar, S., Xu, J.: Hubalign: an accurate and efficient method for global alignment of protein-protein interaction networks. Bioinformatics 30(17), i438–i444 (2014)

    Article  Google Scholar 

  30. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)

    Article  Google Scholar 

  31. Hu, J., Reinert, K.: Localali: an evolutionary-based local alignment approach to identify functionally conserved modules in multiple networks. Bioinformatics 652 (2014)

    Google Scholar 

  32. Kelley, B.P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B.R., Ideker, T.: Pathblast: a tool for alignment of protein interaction networks. Nucleic Acids Res. 32(suppl 2), W83–W88 (2004)

    Article  Google Scholar 

  33. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)

    Google Scholar 

  34. Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., Duesbury, M., Dumousseau, M., Feuermann, M., Hinz, U., et al.: The intact molecular interaction database in 2012. Nucleic Acids Res. 1088 (2011)

    Google Scholar 

  35. Knuth, D.E.: The art of computer programming (1997)

    Google Scholar 

  36. Kollias, G., Sathe, M., Mohammadi, S., Grama, A.: A fast approach to global alignment of protein-protein interaction networks. BMC Res. Notes 6(1), 35 (2013)

    Article  Google Scholar 

  37. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  38. Koyutürk, M., Subramaniam, S., Grama, A.: Functional coherence of molecular networks in bioinformatics. Springer Science & Business Media (2011)

    Google Scholar 

  39. Kuchaiev, O., Milenković, T., Memišević, V., Hayes, W., Pržulj, N.: Topological network alignment uncovers biological function and phylogeny. J. R. Soc. Interface 7(50), 1341–1354 (2010)

    Article  Google Scholar 

  40. Kuchaiev, O., Pržulj, N.: Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 27(10), 1390–1396 (2011)

    Article  Google Scholar 

  41. Lee, J.R., Chung, C.W.: A fast approximation for influence maximization in large social networks. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, Seoul, Korea, pp. 1157–1162. International World Wide Web Conferences Steering Committee (2014)

    Google Scholar 

  42. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Dis. Data 1(1), 2 (2007)

    Article  Google Scholar 

  43. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, pp. 420–429. ACM (2007)

    Google Scholar 

  44. Lü, L., Medo, M., Yeung, C.H., Zhang, Y.C., Zhang, Z.K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)

    Article  Google Scholar 

  45. Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)

    Article  Google Scholar 

  46. Moscato, P., et al.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report 826 (1989)

    Google Scholar 

  47. Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  48. Neyshabur, B., Khadem, A., Hashemifar, S., Arab, S.S.: Netal: a new graph-based method for global alignment of protein-protein interaction networks. Bioinformatics 29(13), 1654–1662 (2013)

    Article  Google Scholar 

  49. Park, D., Singh, R., Baym, M., Liao, C.S., Berger, B.: Isobase: a database of functionally related proteins across ppi networks. Nucleic Acids Res. 39(suppl 1), D295–D300 (2011)

    Article  Google Scholar 

  50. Patro, R., Kingsford, C.: Global network alignment using multiscale spectral signatures. Bioinformatics 28(23), 3105–3114 (2012)

    Article  Google Scholar 

  51. Rahimkhani, K., Aleahmad, A., Rahgozar, M., Moeini, A.: A fast algorithm for finding most influential people based on the linear threshold model. Expert Syst. Appl. 42(3), 1353–1361 (2015)

    Article  Google Scholar 

  52. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  53. Saraph, V., Milenković, T.: Magna: maximizing accuracy in global network alignment. Bioinformatics 30(20), 2931–2940 (2014)

    Article  Google Scholar 

  54. Sharan, R., Suthram, S., Kelley, R.M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R.M., Ideker, T.: Conserved patterns of protein interaction in multiple species. Proc. Nat. Acad. Sci. USA 102(6), 1974–1979 (2005)

    Article  Google Scholar 

  55. Singh, R., Xu, J., Berger, B.: Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc. Nat. Acad. Sci. 105(35), 12763–12768 (2008)

    Article  Google Scholar 

  56. Todor, A., Dobra, A., Kahveci, T.: Probabilistic biological network alignment. IEEE/ACM Trans. Computat. Biol. Bioinform. (TCBB) 10(1), 109–121 (2013)

    Article  Google Scholar 

  57. Uetz, P., Dong, Y.A., Zeretzke, C., Atzler, C., Baiker, A., Berger, B., Rajagopala, S.V., Roupelieva, M., Rose, D., Fossum, E., et al.: Herpesviral protein networks and their interaction with the human proteome. Science 311(5758), 239–242 (2006)

    Article  Google Scholar 

  58. Vijayan, V., Saraph, V., T, M.: Magna++: Maximizing accuracy in global network alignment via both node and edge conservation. Bioinformatics (2015)

    Google Scholar 

  59. Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp. 1039–1048. ACM (2010)

    Google Scholar 

  60. Wernicke, S.: Efficient detection of network motifs. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 3(4), 347–359 (2006)

    Article  Google Scholar 

  61. Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)

    Article  Google Scholar 

  62. Zaslavskiy, M., Bach, F., Vert, J.P.: Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics 25(12), i259–1267 (2009)

    Article  Google Scholar 

  63. Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Proceedings of the 2008 ACM conference on Recommender systems, pp. 123–130. ACM (2008)

    Google Scholar 

  64. Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J.R., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Nat. Acad. Sci. 107(10), 4511–4515 (2010)

    Article  Google Scholar 

  65. Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite network projection and personal recommendation. Phys. Rev. E 76(4), 046,115 (2007)

    Google Scholar 

  66. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms–a comparative case study. In: Eiben, A., Bäck, T., Schoenauer, M., Schwefel, H.P. (eds.) Parallel Problem Solving from Nature–PPSN V, pp. 292–301. Springer, Berlin Heidelberg (1998)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Xidian University, Xi’an, Shaanxi, China

    Maoguo Gong & Shanfeng Wang

  2. Hong Kong Baptist University, Hong Kong, China

    Qing Cai & Lijia Ma

  3. Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Yu Lei

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  1. Maoguo Gong
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  2. Qing Cai
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  3. Lijia Ma
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  4. Shanfeng Wang
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  5. Yu Lei
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Correspondence to Maoguo Gong .

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Gong, M., Cai, Q., Ma, L., Wang, S., Lei, Y. (2017). Real-World Cases of Network Structure Analytics. In: Computational Intelligence for Network Structure Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-4558-5_6

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  • DOI: https://doi.org/10.1007/978-981-10-4558-5_6

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