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KATZ centrality with biogeography-based optimization for influence maximization problem

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Abstract

In the field of social networks, the Influence Maximization Problem (IMP) is one of the most well-known issues that have attracted many researchers in recent years. Influence Maximization (IM) means trying to find the best subset of K nodes that maximizes the number of nodes influenced by this subset. The IM is an NP-hard problem that plays an important role in viral marketing and dissemination of information. The existing solutions like greedy approaches to solving IMP do not have the efficiency and accuracy in solving the problem. In this paper, we propose a new metaheuristic algorithm based on Katz centrality with biogeography-based optimization to solve IMP in the social network. In the proposed algorithm, each habitat with the subset of K nodes is considered as the solution to the IM problem. In the proposed algorithm, the Katz centrality of each node is calculated and used as the emigration rate of each habitat. The focus of the study has been on improving the performance of the BBO algorithm by combining it with the Katz centrality. The objective was to use an enhanced meta-heuristic algorithm with measuring centrality to solve the IM problem. In the results of experiments based on different types of real-world social networks, it is well known that the proposed algorithm is more efficient, accurate, and faster than influence maximization greedy approaches.

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References

  • Bond RM, Fariss CJ, Jones JJ, Kramer AD, Marlow C, Settle JE, Fowler JH (2012) Million-person experiment in social influence and political mobilization. Nature 489(7415):295–298

    Article  Google Scholar 

  • Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining—KDD’09, vol 67, no 1, Paris, p 199

  • Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. SIGKDD, pp 1029–1038

  • Contractor NS, DeChurch LA (2014) Integrating social networks and human social motives to achieve social influence at scale. Proc Nat Acad Sci USA 111:13650–13657

    Article  Google Scholar 

  • Cui L, Hu H, Yu S, Yan Q, Ming Z, Wen Z, Lu N (2018) DDSE: a novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks. J Netw Comput Appl 103:119–130

    Article  Google Scholar 

  • Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, pp 57–66

  • Farswan P, Bansal JC, Deep K (2016) A modified biogeography based optimization, harmony search algorithm. Adv Intell Syst Comput 382:227–238

    Google Scholar 

  • Goyal A, Lu W, Lakshmanan L (2011) Celf ++, optimizing the greedy algorithm for influence maximization in social networks. www, pp 47–48

  • Jendoubi S, Martin A, Liétard L, Hadji HB, Yaghlane BB (2017) Two evidential data based models for influence maximization in Twitter. Knowl-Based Syst 121:58–70

    Article  Google Scholar 

  • Jiang Q, Song G, Cong G, Wang Y, Si W, Xie K (2011) Simulated annealing based influence maximization in social networks. In: Proceedings of the 25th AAAI conference on artificial intelligence, pp 127–132

  • Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18:39–43

    Article  Google Scholar 

  • Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9thACM SIGKDD international conference on knowledge discovery and data mining, pp 137–146

  • Leskovec J, Krause A, Guestrin C, Faloutsos C, Van Briesen J, Glance N (2007) Cost-effective outbreak detection in networks. SIGKDD, pp 420–429

  • Lim WL, Wibowo A, Desa MI, Haron H (2016) A biogeography-based optimization algorithm hybridized with tabu search for the quadratic assignment problem. Comput Intell Neurosci 2:1–12. https://doi.org/10.1155/2016/5803893

    Article  Google Scholar 

  • Lv Z, Song H, Basanta-Val P, Steed A, Jo M (2017) Next-generation big data analytics: state of the art, challenges, and future research topics. IEEE Trans Ind Inform 13(4):1891–1899

    Article  Google Scholar 

  • MacKay DJ (1998) Introduction to Monte Carlo methods. Learning in graphical models. Springer, Berlin, pp 175–204

    MATH  Google Scholar 

  • Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D et al (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(1):1235–1241

    MathSciNet  MATH  Google Scholar 

  • Salehi A, Masoumi B (2019) Participative biogeography-based optimization. J Optim Ind Eng 12(1):79–91

    Google Scholar 

  • Shang J, Wu H, Zhou S, Zhong J, Feng Y, Qiang B (2018) IMPC: influence maximization based on multi-neighbor potential in community networks. Phys A 512:1085–1103

    Article  Google Scholar 

  • Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  • Sumith N, Annappa B, Swapan B (2018) A holistic approach to influence maximization in social networks: STORIE. Appl Soft Comput 66:533–547

    Article  Google Scholar 

  • Tang J, Zhang R, Yao Y, Yang F, Zhao Z, Wang P, Li H, Yuan J (2018) Maximizing the spread of influence via the collective intelligence of discrete bat algorithm. Knowl-Based Syst 160:88–103

    Article  Google Scholar 

  • Tang J, Zhang R, Yao Y, Yang F, Zhao Z, Hu R, Yuan Y (2019) Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization. Phys A 513:477–496

    Article  Google Scholar 

  • Wang F, Jiang W, Li X, Wang G (2018a) Maximizing positive influence spread in online social networks via fluid dynamics. Future Gener Comput Syst 86:1491–1502

    Article  Google Scholar 

  • Wang Y, Dong W, Dong X (2018b) A novel ITÖ algorithm for influence maximization in the large-scale social networks. Future Gener Comput Syst 88:755–763

    Article  Google Scholar 

  • Wen S, Jiang J, Liu B, Xiang Y, Zhou W (2017a) Using epidemic betweenness to measure the influence of users in complex networks. J Netw Comput Appl 78:288–299

    Article  Google Scholar 

  • Wen S, Chen J, Li Y, Shi D, Duan X (2017) Enhancing the performance of biogeography-based optimization using multitopology and quantitative orthogonal learning. Math Probl Eng, p 23

  • Wu W, Du H, Wang H, Wu L, Duan Z, Tian C (2018) On general threshold and general cascade models of social influence. J Comb Optim 35:209–215

    Article  MathSciNet  Google Scholar 

  • Zareie A, Sheikhahmadi A, Khamforoosh K (2018) Influence maximization in social networks based on TOPSIS. Expert Syst Appl 108:96–107

    Article  Google Scholar 

  • Zareie A, Sheikhahmadi A, Jalil M (2019) Identification of influential users in social networks based on users’ interest. Inf Sci 493:217–231

    Article  MathSciNet  Google Scholar 

  • Zareie A, Sheikhahmadi A, Jalil M (2020) Identification of influential users in social network using grey wolf optimization algorithm. Expert Syst Appl 142:112971

    Article  Google Scholar 

  • Zhan J, Gurung S, Parsa SPK (2017) Identification of top-K nodes in large networks using Katz centrality. J Big Data 4:16

    Article  Google Scholar 

  • Zhu Y, Wu W, Bi Y, Wu L, Jiang Y, Xu W (2015) Better approximation algorithms for influence maximization in online social networks. J Comb Optim 30:97–108

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Abbas Salehi & Behrooz Masoumi

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  1. Abbas Salehi
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  2. Behrooz Masoumi
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Correspondence to Behrooz Masoumi.

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Salehi, A., Masoumi, B. KATZ centrality with biogeography-based optimization for influence maximization problem. J Comb Optim 40, 205–226 (2020). https://doi.org/10.1007/s10878-020-00580-6

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