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Using Network Alignment to Identify Conserved Consumer Behaviour Modelling Constructs

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Business and Consumer Analytics: New Ideas

Abstract

Extracting topological information from networks is a central problem in many fields including business analytics. With the increase in large-scale datasets, effectively comparing similarities and differences between networks is impossible without automation. In some cases, computational search of simple subgraphs is used to understand the structure of a network. These approaches, however, miss the “global picture” of network similarity. Here we examine the Network Alignment problem, in which we look for a mapping between vertex sets of two networks preserving topological information. Elsewhere, we showed that data analytics problems are often of varied computational complexity. We prove that this problem is W[1]-complete for several parameterizations. Since we expect large instances in the data analytics field, our result indicates that this problem is a prime candidate for metaheuristic approaches as it will be hard in practice to solve exact methods. We develop a memetic algorithm and demonstrate the effectiveness of the Network Alignment problem as a tool for discovering structural information through an application in the area of consumer behaviour modelling. We believe this to be the first demonstration of such an approach in the social sciences and in particular a consumer analytics application.

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Notes

  1. 1.

    http://bioinfo.vanderbilt.edu/gpu-fan/.

  2. 2.

    DEAP Source code: https://github.com/DEAP/deap/.

  3. 3.

    DEAP Documentation: http://deap.readthedocs.org/en/master/.

  4. 4.

    deap.tools.mutShuffleIndexes.

  5. 5.

    deap.tools.cxPartialyMatched.

  6. 6.

    deap.tools.selTournament.

  7. 7.

    For those unfamiliar and interested in the technicalities of complexity theory, see [13, 16, 19].

References

  1. J. Abello, A. L. Buchsbaum, and J. R. Westbrook. A functional approach to external graph algorithms. Algorithmica, 32(3):437–458, 2002.

    Article  MathSciNet  Google Scholar 

  2. Giovanni Acampora, Pasquale Avella, Vincenzo Loia, Saverio Salerno, and Autilia Vitiello. Improving ontology alignment through memetic algorithms. In Proceedings of the IEEE International Conference on Fuzzy Systems, pages 1783–1790. IEEE, 2011.

    Google Scholar 

  3. M. Barbany, H. Gutiérrez de Terán, F. Sanz, and J. Villà-Freixa. Towards a mip-based alignment and docking in computer-aided drug design. Proteins, 56(3):585–594, 2004.

    Article  Google Scholar 

  4. J. Carlson and A. O’Cass. Creating commercially compelling website-service encounters: an examination of the effect of website-service interface performance components on flow experiences. Electronic Markets, 21:237–253, 2011.

    Article  Google Scholar 

  5. J. Carlson and A. O’Cass. Optimizing the online channel in professional sport to create trusting and loyal consumers: The role of the professional sports team brand and service quality. Journal of Sport Management, 26:463, 2012.

    Article  Google Scholar 

  6. Stephen A. Cook. The complexity of theorem-proving procedures. In Proceedings of the Third Annual ACM Symposium on Theory of Computing, pages 151–158. ACM, 1971.

    Google Scholar 

  7. G. A. Croes. A method for solving traveling salesman problems. Operations Research, 6:791–812, 1958.

    Article  MathSciNet  Google Scholar 

  8. I. P. Cvijikj and F. Michahelles. Online engagement factors on Facebook brand pages. 3(4):843–861, 2013.

    Google Scholar 

  9. Natalie J de Vries, Jamie Carlson, and Pablo Moscato. A data-driven approach to reverse engineering customer engagement models: Towards functional constructs. PLoS ONE, 9(7), 2014.

    Google Scholar 

  10. Natalie J de Vries, Ahmed S Arefin, and Pablo Moscato. Gauging heterogeneity in online consumer behaviour data: A proximity graph approach. In 2014 IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud), pages 485–492. IEEE, 2014.

    Google Scholar 

  11. Remco Dijkman, Marlon Dumas, and Luciano García-Bañuelos. Business Process Management: 7th International Conference, BPM 2009, Ulm, Germany, September 8–10, 2009. Proceedings, chapter Graph Matching Algorithms for Business Process Model Similarity Search, pages 48–63. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009.

    Google Scholar 

  12. Remco Dijkman, Marlon Dumas, and Luciano García-Bañuelos. Graph Data Management: Techniques and Applications, chapter Business Process Graphs: Similarity Search and Matching, pages 421–437. ICI Global, Hershey, 2012.

    Google Scholar 

  13. Rodney G. Downey and Michael R. Fellows. Fundamentals of Parameterized Complexity. Texts in Computer Science. Springer, 2013.

    Book  Google Scholar 

  14. S. P. Ficklin and F. A. Feltus. Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice. Plant Physiology, 156(3):1244–56, 2011.

    Article  Google Scholar 

  15. Jörg Flum and Martin Grohe. Fixed-parameter tractability, definability, and model checking. SIAM Journal on Computing, 31(1):113–145, 2001.

    Article  MathSciNet  Google Scholar 

  16. Jörg Flum and Martin Grohe. Parameterized Complexity Theory. Texts in Theoretical Computer Science. Springer, 2006.

    MATH  Google Scholar 

  17. Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13:2171–2175, Jul 2012.

    MathSciNet  MATH  Google Scholar 

  18. Linton Freeman. A set of measures of centrality based on betweenness. Sociometry, 40:35–41, 1977.

    Article  Google Scholar 

  19. Michael R. Garey and David S. Johnson. Computers and intractability: A guide to the theory of NP-completeness. W. H. Freeman and Co., San Francisco, Calif., 1979.

    Google Scholar 

  20. David E. Goldberg and Robert Lingle Jr. Alleles, loci, and the traveling salesman problem. In John J. Grefenstette, editor, Proceedings of the First International Conference on Genetic Algorithms and Their Applications. Lawrence Erlbaum Associates, Publishers, 1985.

    Google Scholar 

  21. N. Hohenstein, M. J. Sirgy, A. Herrmann, and M. Heitmann. Self-congruity: Antecedents and consequences. In 34th La Londe International Research Conference in Marketing Communications and Consumer Behaviour, pages 118–130, Aix en Provance, France, 2007.

    Google Scholar 

  22. Yannick Hold-Geoffroy, Olivier Gagnon, and Marc Parizeau. Once you SCOOP, no need to fork. In Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, page 60. ACM, 2014.

    Google Scholar 

  23. L. Hubert and P. Arabie. Comparing partitions. Journal of Classifications, 2:193–218, 1985.

    Article  Google Scholar 

  24. B. Jahn and W. Kunz. How to transform consumers into fans of your brand. Journal of Service Management, 23(3):344–361, 2012.

    Article  Google Scholar 

  25. Gunnar W. Klau. A new graph-based method for pairwise global network alignment. BMC Bioinformatics, 10(Suppl 1):S59, 2009.

    Google Scholar 

  26. A. M. Liebetrau. Measures of Association. SAGE Publications, Inc., Thousand Oaks, CA, 1983.

    Book  Google Scholar 

  27. J. Marenco. Un algoritmo branch-and-cut para el problema de mapping. Master’s thesis, Departamento de Computación, Universidade de Buenos Aires, 1999.

    Google Scholar 

  28. A. O’Cass and J. Carlson. Examining the effects of website-induced flow in professional sporting team websites. Internet Research, 20:115–134, 2010.

    Article  Google Scholar 

  29. A. O’Cass and J. Carlson. An empirical assessment of consumers’ evaluations of web site service quality: Conceptualizing and testing a formative model. Journal of Services Marketing, 26:419–434, 2012.

    Article  Google Scholar 

  30. A. O’Cass and L. Ngo. Examining the firm’s value creation process: A managerial perspective of the firm’s value offering strategy and performance. British Journal of Management, 22:646–671, 2011.

    Article  Google Scholar 

  31. J. van Doorn, K. N. Lemon, V. Mittal, S. Nass, D. Pick, P. Pirnir, and P. C. Verhoef. Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3):253–266, 2010.

    Article  Google Scholar 

  32. Autilia Vitiello. Memetic algorithms for ontology alignment. PhD thesis, Universita degli studi di Salerno, 2012.

    Google Scholar 

  33. Xingsi Xue and Yuping Wang. Optimizing ontology alignments through a memetic algorithm using both matchfmeasure and unanimous improvement ratio. Artificial Intelligence, 223(0):65–81, 2015.

    Article  MathSciNet  Google Scholar 

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Acknowledgements

Pablo Moscato acknowledges previous support from the Australian Research Council Future Fellowship FT120100060 and Australian Research Council Discovery Projects DP120102576 and DP140104183.

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Authors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Luke Mathieson, Natalie Jane de Vries & Pablo Moscato

  2. School of Software, University of Technology Sydney, Ultimo, NSW, Australia

    Luke Mathieson

Authors
  1. Luke Mathieson
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  2. Natalie Jane de Vries
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  3. Pablo Moscato
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Correspondence to Luke Mathieson .

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Editors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Pablo Moscato

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries

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Mathieson, L., de Vries, N.J., Moscato, P. (2019). Using Network Alignment to Identify Conserved Consumer Behaviour Modelling Constructs. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-06222-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06221-7

  • Online ISBN: 978-3-030-06222-4

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