Centrality measures, upper bound, and influence maximization in large scale directed social networks

Fundamenta Informaticae

Volumn 130, Issue 3, 2014, Pages 317-342

  Pal, Sankar K ^a   Kundu, Suman ^a   Murthy, C A ^a  

^a INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Centrality measure; Independent cascade model; Influence maximization; Social network; Statistical significance

Indexed keywords

EID: 84894547458     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: 10.3233/FI-2014-994     Document Type: Article

References (27)

1. Apache Commons Math (TTest) Documentation.
2. Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques, Pervasive and Mobile Computing, 6(2), 2010, 161-180.
3. Brézillon, P.: Context in problem solving: a survey, The Knowledge Engineering Review, 14(01), 1999, 47-80.
4. Chen, W., Wang, C., Wang, Y.: 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, ACM, 2010.
5. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2009.
6. Chen,W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model, 2010 IEEE International Conference on Data Mining, IEEE, 2010, ISSN 1550-4786.
7. Choudhury, M. D., Sundaram, H., John, A., Seligmann, D. D., Kelliher, A.: "Birds of a Feather": Does User Homophily Impact Information Diffusion in Social Media?, CoRR, abs/1006.1702, 2010.
8. Dolecek, L., Shah, D.: Influence in a large society: Interplay between information dynamics and network structure, 2009 IEEE International Symposium on Information Theory, IEEE, June 2009, ISBN 978-1-4244- 4312-3.
9. Domingos, P., Richardson, M.: Mining the network value of customers, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2001.
10. Estevez, P. a., Vera, P., Saito, K.: Selecting the Most Influential Nodes in Social Networks, 2007 International Joint Conference on Neural Networks, August 2007, 2397-2402, ISSN 1098-7576.
11. Estevez, P. a., Vera, P., Saito, K.: Selecting the Most Influential Nodes in Social Networks, 2007 International Joint Conference on Neural Networks, IEEE, August 2007, ISBN 978-1-4244-1379-9, ISSN 1098-7576.
12. Freeman, L.: Centrality in social networks conceptual clarification, Social networks, 1(3), 1979, 215-239, ISSN 0378-8733.
13. Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth, Marketing Letters, 12(3), 2001, 211-223, ISSN 0923-0645.
14. Goldenberg, J., Libai, B., Muller, E.: Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata, Academy of Marketing Science Review, 9(3), 2001, 1-18.
15. Goyal, A., Lu, W., Lakshmanan, L.: CELF++: optimizing the greedy algorithm for influence maximization in social networks, Proceedings of the 20th international conference companion on World wide web, ACM, 2011.
16. Granovetter, M.: Threshold models of collective behavior, The American Journal of Sociology, 83(6), 1978, 1420-1443, ISSN 0002-9602.
17. Jeong, H., Albert, R.: Diameter of the world-wide web, Nature, 401(September), 1999, 398-399.
18. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03, ACM Press, New York, New York, USA, 2003, ISBN 1581137370.
19. Kimura, M., Saito, K.: Tractable Models for Information Diffusion in Social Networks, Principles of Data Mining and Knowledge Discovery, 2006.
20. Leskovec, J., Adamic, L. a., Huberman, B. a.: The dynamics of viral marketing, ACM Transactions on the Web, 1(1), May 2007, 5-es, ISSN 15591131.
21. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2007.
22. Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M.: Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters, Internet Mathematics, 6(1), 2009, 29-123.
23. McCarthy, J.: Notes on formalizing context, Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1, Morgan Kaufmann Publishers Inc., Chambery, France, 1993.
24. Narayanam, R., Narahari, Y.: A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks, IEEE Transactions on Automation Science and Engineering, 8(1), 2011, 130-147.
25. Nieminen, J.: On the Centrality in a Graph, Scandinavian Journal of Psychology, 15, September 1974, 332-336.
26. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02, 2002, 61.
27. Rogers, E. M.: Diffusion of Innovations, The Free Press of Glencoe, New York, 1962.