Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Hot spot DBC: Location based information diffusion for marketing strategy in mobile social networks

Hotspot DBC: 모바일 소셜 네트워크 상에서 마케팅 전략을 위한 위치 기반 정보 유포

  • Ryu, Jegwang (The Department of Computer Science, Yonsei University) ;
  • Yang, Sung-Bong (The Department of Computer Science, Yonsei University)
  • 류제광 (연세대학교 공과대학 컴퓨터과학과) ;
  • 양성봉 (연세대학교 공과대학 컴퓨터과학과)
  • Received : 2017.03.18
  • Accepted : 2017.05.15
  • Published : 2017.06.30
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Abstract

As the advances of technology in mobile networking and the popularity of online social networks (OSNs), the mobile social networks (MSNs) provide opportunities for marketing strategy. Therefore, understanding the information diffusion in the emerging MSNs is a critical issue. The information diffusion address a problem of how to find the proper initial nodes who can effectively propagate as widely as possible in the minimum amount of time. We propose a new diffusion scheme, called Hotspot DBC, which is to find k influential nodes considering each node's mobility behavior in the hotspot zones. Our experiments were conducted in the Opportunistic Network Environment (ONE) using real GPS trace, to show that the proposed scheme results. In addition, we demonstrate that our proposed scheme outperforms other existing algorithms.

모바일 디바이스의 무선 네트워크 통신 기술과 온라인 소셜 네트워크 발전으로 모바일 소셜 네트워크는 모바일 기기 사이에 마케팅 전략의 기회를 제공한다. 이에 따라 모바일 소셜 네트워크 상에서 정보 유포는 중요한 문제가 되었으며 여러 기법을 제안해왔다. 정보 유포 연구 정의는 메시지와 같은 정보를 가진 초기 노드로부터 최소한의 시간에 최대한 많은 유저에게 정보를 전달하는 기법이다. 본 논문에서 우리는 새로운 정보 유포 기법인 기계학습과 소셜 위치정보 기반의 Hotspot DBC를 제안한다. 위치기반 정보 유포 기법으로써 핫스팟 지역을 사용한다. 웜업 기간에 움직임 패턴을 활용하여 초기 영향력 있는 노드를 찾는다. 이후 전체 네트워크 지역을 고려하는 것이 아닌 특정 핫스팟 지역에서만 패턴을 추출하여 찾는다. 웜업 기간 끝나는 시점에서 각 노드는 움직임 패턴을 추출한다. 마지막으로 각 패턴에서 소셜 관계를 분석함으로써 영향력 있는 노드 k개가 선정된다. 우리는 기회적 네트워크 환경에서 GPS 위치 기록의 실제 모바일 노드의 움직임 데이터를 ONE 시뮬레이터 환경에서 실험하였다. 추가적으로 통신범위와 초기 정보 유포 k 노드 수를 다양하게 실험하여 기존 기법보다 더 나은 결과를 확인할 수 있다.

Keywords

References

  1. Ma, H., H. Yang, M. R. Lyu and I. King, "Mining social networks using heat diffusion processes for marketing candidates selection," Proceedings of the 17th ACM conference on Information and knowledge management, (2008), 233-242.
  2. Richardson, M. and P. Domingos, "Mining knowledge-sharing sites for viral marketing," Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, (2002), 61-70.
  3. Conti, S., Giordano, M. May, and A. Passarella, "From opportunistic networks to opportunistic computing," IEEE Communications Magazine, Vol.48, No. 9(2010), 126-139. https://doi.org/10.1109/MCOM.2010.5560597
  4. Chen, W., C. Wang and Y. Wang, "Scalable influence maximization for prevalent viral marketing in large-scale social networks," Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, (2010), 1029-1038.
  5. Lu, Z., Y. Wen and G. Cao, "Information diffusion in mobile social networks: The speed perspective," Proceedings of IEEE INFOCOM, (2014), 1932-1940.
  6. Chen, X. and K. Xiong, "Dynamic social feature-based diffusion in mobile social networks," Proceedings of IEEE/CIC International Conference on Communications in China (ICCC), (2015), 1-6.
  7. Myers S. A., C. Zhu and J. Leskovec, "Information diffusion and external influence in networks," Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, (2012), 33-41.
  8. Tsai T. C. and H. H. Chan, "NCCU Trace: social-network-aware mobility trace," IEEE Communications Magazine, Vol.53, No. 10(2015), 144-149. https://doi.org/10.1109/MCOM.2015.7295476
  9. Domingos P. and M. Richardson, "Mining the network value of customers," Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining, (2001), 57-66.
  10. Kempe D., J. Kleinberg and E. Tardos, "Maximizing the spread of influence through a social network," Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, (2003), 137-146.
  11. Wang Y., G. Cong, G. Song and K. Xie, "Community-based greedy algorithm for mining top-k influential nodes in mobile social networks," Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, (2010), 1039-1048.
  12. Christakis N. A. and J. H. Fowler, "The spread of obesity in a large social network over 32 years," New England journal of medicine, Vol.357, No. 4(2007), 370-379. https://doi.org/10.1056/NEJMsa066082
  13. Bakshy E., I. Rosenn, C. Marlow and L. Adamic, "The role of social networks in information diffusion," Proceedings of the 21st international conference on World Wide Web. ACM, (2012), 519-528.
  14. Lopez-Pintado D., "Diffusion in complex social networks," Games and Economic Behavior, Vol.62, No. 2(2008), 573-590. https://doi.org/10.1016/j.geb.2007.08.001
  15. Han, B., P. Hui, V. S. A. Kumar, M. V. Marathe, J. Shao and A. Srinivasan, "Mobile data offloading through opportunistic communications and social participation," IEEE Transactions on Mobile Computing, Vol.11, No. 5(2012), 821-834. https://doi.org/10.1109/TMC.2011.101
  16. Ester M., H. P. Kriegel, J. Sander and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," kdd, Vol.96 No. 34(1996), 226-231.
  17. Opsahl T., F. Agneessens and J. Skvoretz, "Node centrality in weighted networks: Generalizing degree and shortest paths," Social networks, Vol.32 No. 3(2010), 245-251. https://doi.org/10.1016/j.socnet.2010.03.006
  18. Keranen A., J. Ott and T. Karkkainen, "The ONE simulator for DTN protocol evaluation," Proceedings of the 2nd International Conference on Simulation Tools and Techniques, (2009), 1-10.