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Computing an effective decision making group of a society using social network analysis

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Abstract

Recent years have witnessed how much a decision making group can be dysfunctional due to the extreme hyperpartisanship. While partisanship is crucial for the representatives to pursue the wishes of those whom they represent for, such an extremism results in a severe gridlock in the decision making progress, and makes themselves highly inefficient. It is known that such a problem can be mitigated by having negotiators in the group. This paper investigates the potential of social network analysis techniques to choose an effective leadership group of a society such that it suffers less from the extreme hyperpartisanship. We establish three essential requirements for an effective representative group, namely Influenceability, Partisanship, and Bipartisanship. Then, we formulate the problem of finding a minimum size representative group satisfying the three requirements as the minimum connected \(k\)-core dominating set problem (MC\(k\)CDSP), and show its NP-hardness. We introduce an extension of MC\(k\)CDSP, namely MC\(k\)CDSP-C, which assumes the society has a number of sub-communities and requires at least one representative from each sub-community should be in the leadership. We also propose an approximation algorithm for a subclass of MC\(k\)CDSP with \(k=2\), and show an \(\alpha \)-approximation algorithm of MC\(k\)CDSP can be used to obtain an \(\alpha \)-approximation algorithm of MC\(k\)CDSP-SC.

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References

  • Dinh TN, Shen Y, Nguyen DT, Thai MT (2012) On the approximability of positive influence dominating set in social networks. J Comb Optim. doi:10.1007/s10878-012-9530-7

  • Eubank S, Anil Kumar VS, Marathe MV, Srinivasan A, Wang N (2004) Structural and algorithmic aspects of massive social networks. In: Proceedings of the 15th ACM-SIAM symposium on discrete algorithms (SODA), p 718–727, 2004

  • Garey MR, Johnson DS (1978) Computers and intractability: a guide to the theory of NP-completeness. Freeman, San Francisco

    Google Scholar 

  • Guha S, Khuller S (1996) Approximation algorithms for connected dominating sets. Algorithmica 20:374–387

    Article  MathSciNet  Google Scholar 

  • Kelleher LL, Cozzens MB (1988) Dominating sets in social network graphs. Math Soc Sci 16(3):267–279

    Article  MATH  MathSciNet  Google Scholar 

  • Kim D, Li D, Asgari O, Li Y, Tokuta AO (2013) A dominating set based approach to identify effective leader group of social network. In: Proceedings of the workshop on computational social networks (CSoNet 2013)

  • Klein E (2012) 14 reasons why this is the worst Congress ever. The Washington Post, July 13, 2012. Available at http://www.washingtonpost.com/blogs/wonkblog/wp/2012/07/13/13-reasons-why-this-is-the-worst-congress-ever/. Accessed 2 Jul 2013

  • Mann TE, Ornstein NJ (2012) It’s even worse than it looks: how the American Constitutional System collided with the new politics of extremism. Basic Books, New York

    Google Scholar 

  • Wang F, Camacho E, Xu K (2009) Positive influence dominating set in online social networks. In: Proceedings of the 3rd international conference on combinatorial optimization and applications (COCOA), pp. 313–321

  • Zhu X, Yu J, Lee W, Kim D, Shan S, Du D-Z (2010) New dominating sets in social networks. J Glob Optim 48(4):633–642

    Article  MATH  MathSciNet  Google Scholar 

  • Zou F, Zhang Z, Wu W (2009) Latency-bounded minimum influential node selection in social networks. In: Proceedings of workshop on social networks, applications, and systems, 2009

Download references

Acknowledgments

This work was supported in part by US National Science Foundation (NSF) CREST No. HRD-0833184 and by US Army Research Office (ARO) No. W911NF-0810510. This research was jointly supported in part by National Natural Science Foundation of China under Grants 61070191 and 91124001, and Shenzhen Strategic Emerging Industries Program with Grant No. ZDSY20120613125016389. This research was supported by the MSIP (Ministry of Science, ICT&Future Planning, Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-1002) supervised by the NIPA (National IT Industry Promotion Agency), by the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Education, Science and Technology (No. 2012-R1A2A2A01046986), and by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MEST) (No. 2012-R1A1A2009152).

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Correspondence to Donghyun Kim.

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The preliminary version of this paper has been appeared in the Proceedings of the Workshop on Computational Social Networks (CSoNet 2013) Kim et al. (2013).

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Kim, D., Li, D., Asgari, O. et al. Computing an effective decision making group of a society using social network analysis. J Comb Optim 28, 577–587 (2014). https://doi.org/10.1007/s10878-013-9687-8

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