Abstract
New node centrality measurement for directed networks called the Sales Potential is introduced with the property that nodes with high Sales Potential have small in-degree and high second-shell in-degree. Such nodes are of great importance in online marketing strategies for sales agents and IT security in social networks. We propose an optimization problem that aims at finding a finite set of nodes, so that their collective Sales Potential is maximized. This problem can be efficiently solved with a Quasi-parallel Genetic Algorithm defined on a given topology of sub-populations. We find that the algorithm with a small number of sub-populations gives results with higher quality than one with a large number of sub-populations, though at the price of slower convergence.
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References
Barabasi, A.-L., Albert, R.: Emergence of Scaling in Random Networks. Science 5439, 509–511 (1999)
Newman, M.E.J.: Networks: An Introduction. Oxford University Press, Oxford (2010)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: Genetic algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Li, S.P., Szeto, K.Y.: Crytoarithmetic problem using parallel Genetic algorithms. In: Mendl 1999, Brno, Czech (1999)
Szeto, K.Y., Cheung, K.H.: Multiple time series prediction using genetic algorithms optimizer. In: Proceedings of the International Symposium on Intelligent Data Engineering and Learning, IDEAL 1998, Hong Kong, pp. 127–133 (1998)
Jiang, R., Szeto, K.Y., Luo, Y.P., Hu, D.C.: Distributed parallel genetic algorithms with path splitting scheme for the large traveling salesman problems. In: Shi, Z., Faltings, B., Musen, M. (eds.) Proceedings of Conference on Intelligent Information Processing, 16th World Computer Congress 2000, Beijing, August 21-25, pp. 478–485. Publishing House of Electronic Industry (2000)
Szeto, K.Y., Cheung, K.H., Li, S.P.: Effects of dimensionality on parallel genetic algorithms. In: Proceedings of the 4th International Conference on Information System, Analysis and Synthesis, Orlando, Florida, USA, vol. 2, pp. 322–325 (1998)
Szeto, K.Y., Fong, L.Y.: How Adaptive Agents in Stock Market Perform in the Presence of Random News: A Genetic Algorithm Approach. In: Leung, K.-S., Chan, L., Meng, H. (eds.) IDEAL 2000. LNCS (LNAI), vol. 1983, pp. 505–510. Springer, Heidelberg (2000)
Fong, A.L.Y., Szeto, K.Y.: Rule Extraction in Short Memory Time Series using Genetic algorithms. European Physical Journal B 20, 569–572 (2001)
Shiu, K.L., Szeto, K.Y.: Self-adaptive Mutation Only Genetic Algorithm: An Application on the Optimization of Airport Capacity Utilization. In: Fyfe, C., Kim, D., Lee, S.-Y., Yin, H. (eds.) IDEAL 2008. LNCS, vol. 5326, pp. 428–435. Springer, Heidelberg (2008)
Chen, C., Guan, W., Szeto, K.Y.: Markov Chains Genetic algorithms for Airport Scheduling. In: Proceedings of the 9th International FLINS Conference on Foundations and Applications of Computational Intelligence (FLINS 2010), pp. 905–910 (August 2010)
Wang, G., Wu, D., Szeto, K.Y.: Quasi-Parallel Genetic Algorithms with Different Communication Topologies. In: 2011 IEEE Congress on Evolutionary Computation (CEC), June 5-8, pp. 721–727 (2011)
Wang, G., Wu, D., Chen, W., Szeto, K.Y.: Importance of Information Exchange in Quasi-Parallel Genetic Algorithms. In: Krasnogor, N. (ed.) Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO 2011), pp. 127–128. ACM, New York (2011)
Aboav, D.A.: Metallography V.3, 383 (1970); ibid, V.13, 43 (1980)
Weaire, D.: Metallography. Â 7, 157 (1974)
Ma, C.W., Szeto, K.Y.: Phys. Rev. EÂ 73, 047101 (2006)
Szeto, K.Y., Fu, X., Tam, W.Y.: Universal Topological Properties of Two-dimensional Cellular Patterns. Phys. Rev. Lett., 138302-1–138302-3 (2002)
Ma, C.W., Szeto, K.Y.: Locus Oriented Adaptive Genetic algorithms: Application to the Zero/One Knapsack Problem. In: Proceeding of the 5th International Conference on Recent Advances in Soft Computing, RASC 2004, Nottingham, UK, pp. 410–415 (2004)
Law, N.L., Szeto, K.Y.: Adaptive Genetic algorithms with Mutation and Crossover Matrices. In: Proceeding of the 12th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, January 6-12. Theme: Al and Its Benefits to Society, vol. II, pp. 2330–2333 (2007)
Szeto, K.Y., Zhang, J.: Adaptive Genetic Algorithm and Quasi-parallel Genetic Algorithm: Application to Knapsack Problem. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2005. LNCS, vol. 3743, pp. 189–196. Springer, Heidelberg (2006)
Bondi, A.B.: Characteristics of scalability and their impact on performance. In: Proceedings of the 2nd International Workshop on Software and Performance, pp. 195–203 (2000)
Moore, G.E.: Cramming more components onto integrated circuits. Electronics Magazine, 4 (1965)
Cantú-Paz, E.: Efficient and accurate parallel genetic algorithms, pp. 16, 17, 22. Kluwer Academic, USA (2000)
Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M.: Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics 6(1), 29–123 (2009)
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Wang, C.G., Szeto, K.Y. (2012). Sales Potential Optimization on Directed Social Networks: A Quasi-Parallel Genetic Algorithm Approach. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_12
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DOI: https://doi.org/10.1007/978-3-642-29178-4_12
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