KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Influence Maximization against Social Adversaries

소셜 네트워크 내 경쟁 집단에의 영향력 최대화 기법

  • 정시현 (서울대학교 컴퓨터공학부) ;
  • 노기섭 (서울대학교 컴퓨터공학부) ;
  • 오하영 (숭실대학교 정보통신전자공학부) ;
  • 김종권 (서울대학교 컴퓨터공학부)
  • Received : 2014.08.29
  • Accepted : 2014.11.13
  • Published : 2015.01.15
⟨ Previous Next ⟩

Abstract

Online social networks(OSN) are very popular nowadays. As OSNs grows, the commercial markets are expanding their social commerce by applying Influence Maximization. However, in reality, there exist more than two players(e.g., commercial companies or service providers) in this same market sector. To address the Influence Maximization problem between adversaries, we first introduced Influence Maximization against the social adversaries' problem. Then, we proposed an algorithm that could efficiently solve the problem efficiently by utilizing social network properties such as Betweenness Centrality, Clustering Coefficient, Local Bridge and Ties and Triadic Closure. Moreover, our algorithm performed orders of magnitudes better than the existing Greedy hill climbing algorithm.

최근 온라인 소셜 네트워크의 성장에 따라, 영향력 최대화 기법을 활용한 다양한 마케팅 기법들이 소개되고 있다. 하지만 지금까지 네트워크 구성이 감춰진 경쟁 집단들이 존재하는 환경에서 영향력 최대화 문제를 해결하려고 시도한 기법은 제안된 적이 없었다. 본 논문에서는 아군 집단과 경쟁 집단 들이 존재하는 소셜 네트워크 환경에서 경쟁 집단에 영향력을 가장 최대화하는 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘은 소셜 네트워크의 속성들 중 중간 중심성, 클러스터링 계수, 지역적 연결도로와 연결, 그리고 3인조 폐쇄특징 등을 효과적으로 활용한다. 실험을 통하여 본 논문에서 제안하는 알고리즘이 기존 알고리즘보다 경쟁 집단에의 영향력을 더 확산할 수 있음을 확인하였고, 결론적으로 2배의 성능 향상을 보여 주었다.

Keywords

Acknowledgement

Grant : HD 급 미디어의 양방향 실시간 전송 및 제어가 가능한 유무선 i-AVB 시스템 기술개발

Supported by : 한국산업기술평가관리원, 한국연구재단

References

  1. D. Kempe, J. Kleinberg, and E. Tardos, "Maximizing the spread of influence through a social network," Proc. of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137-146, 2003.
  2. W. Chen, Y. Wang, and S. Yang, "Efficient influence maximization in social networks," Proc. of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 199-208, 2009.
  3. M. Granovetter, "The strength of weak ties," American journal of sociology, Vol. 78, p. 1, 1973.
  4. D. Easley and J. Kleinberg, "Networks, Crowds, and Markets: Reasoning about a highly connected world," ed: Wiley Online Library, 2012.
  5. W. Chen, Y. Yuan, and L. Zhang, "Scalable influence maximization in social networks under the linear threshold model," Data Mining (ICDM), 2010 IEEE 10th International Conference on, pp. 88-97, 2010.
  6. K. Saito, R. Nakano, and M. Kimura, "Prediction of information diffusion probabilities for independent cascade model," Knowledge-Based Intelligent Information and Engineering Systems, pp. 67-75, 2008.
  7. Borodin, Allan, Y. Filmus, and J. Oren, "Threshold models for competitive influence in social networks," Internet and network economics, pp. 539-550, 2010.
  8. He, Xinran, et al., "Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model," SDM, 2012.
  9. Bharathi, Shishir, D. Kempe, and M. Salek. "Competitive influence maximization in social networks," Internet and Network Economics, Springer Berlin Heidelberg, pp. 306-311, 2007.
  10. Shirazipourazad, Shahrzad, et al., "Influence propagation in adversarial setting: how to defeat competition with least amount of investment," Proc. of the 21st ACM international conference on Information and knowledge management, ACM, 2012.
  11. Yu, Yintao, Tanya Y. Berger-Wolf, and Jared Saia, "Finding spread blockers in dynamic networks," Advances in Social Network Mining and Analysis, Springer Berlin Heidelberg, pp. 55-76, 2010.
  12. Bharathi, Shishir, D. Kempe, and M. Salek, "Competitive influence maximization in social networks," Internet and Network Economics, Springer Berlin Heidelberg, pp. 306-311, 2007.