Discovering Influential Nodes in Social Networks through Community Finding

Jerry Scripps

2013

Abstract

Finding influential nodes in a social network has many practical applications in such areas as marketing, politics and even disease control. Proposed methods often take greedy approaches to find the best k nodes to activate so that the diffusion of activation will spread to the largest number of nodes. In this paper, we study the effects of using a community finding approach to not only maximize the number of activated nodes but to also spread the activation to more segments of the network. After describing our approach we present experiments that explain the effects of this approach.

References

  1. Bharathi, S., Kempe, D., and Salek, M. (2007). Competitive influence maximization in social networks. In Proceedings of WINE.
  2. Chen, W., Wang, Y., and Yang, S. (2009). Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.
  3. Chen, Y. C., Chang, S. H., Chou, C. L., Peng, W. C., and Lee, S. Y. (2012). Exploring community structures for influence maximization in social networks. In Proceedings of SNA-KDD.
  4. Clauset, A., Moore, C., and J.Newman, M. E. (2006). Structural inference of hierarchies in networks. In Proceedings of the 23rd International Conference on Machine Learning (ICML), Workshop on Social Network Analysis.
  5. Domingos, P. and Richardson, M. (2001). Mining the network value of customers. In Proceedings of the Seveth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 57-66.
  6. Goldenberg, J., Libai, B., and Muller, E. (2001). Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata. Academy of Marketing, 01.
  7. Granovetter, M. (1978). Threshold models of collective behavior. The American Journal of Sociology, 83.
  8. Jain, A. and Dubes, R. (1988). Algorithms for clustering data. Prentice-Hall, Inc.
  9. Kempe, D., Kleinberg, J., and Tardos, E. (2003). Maximizing the spread of influence through a social network.
  10. Narayanam, R. and Narahari, Y. (2011). A shapley valuebased approach to discover influential nodes in social networks. Automation Science and Engineering, IEEE Transactions on, 8(1):130 -147.
  11. Newman, M. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69.
  12. Pearson, M., Steglich, C., and Snijders, T. (2006). Homophily and assimilation among sport-active adolescent substance users. Connections, 27:47-63.
  13. Porter, M., Onnela, J., and Mucha, P. (2009). Communities in networks. Notices of the American Mathematical Society, 56.
  14. Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions On Pattern Analysis And Machine Intelligence, 22(8).
  15. Tang, L., Wang, X., Liu, H., and Wang, L. (2010). A multiresolution approach to learning with overlapping communities. In KDD Workshop on Social Media Analytics.
  16. Wang, Y., Cong, G., Song, G., and Xie, K. (2010). Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of SIGKDD.
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Paper Citation


in Harvard Style

Scripps J. (2013). Discovering Influential Nodes in Social Networks through Community Finding . In Proceedings of the 9th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8565-54-9, pages 403-412. DOI: 10.5220/0004350704030412


in Bibtex Style

@conference{webist13,
author={Jerry Scripps},
title={Discovering Influential Nodes in Social Networks through Community Finding},
booktitle={Proceedings of the 9th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2013},
pages={403-412},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004350704030412},
isbn={978-989-8565-54-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Discovering Influential Nodes in Social Networks through Community Finding
SN - 978-989-8565-54-9
AU - Scripps J.
PY - 2013
SP - 403
EP - 412
DO - 10.5220/0004350704030412