Abstract
While most existing work about community focus on the community structure and the tendency of one individual joining a community; equally important is to understand social influence from community and to find strategies of attracting new members to join the community, which may benefit a range of applications. In this paper, we formally define the problem of community expansion in social network, which is under the marketing promotional activities scenario. We propose three models, Adopter Model, Benefit Model and Combine Model, to present different promotion strategies over time, taking into consideration the community structure characters. Specifically, Adopter Model is based on the factors that can make an individual come into a community. Benefit Model considers the factors that attract more new members. Combine Model aims to find a balance between Adopter Model and Benefit Model. Then a greedy algorithm ETC is developed for expanding a community over time. Our results from extensive simulation on several real-world networks demonstrate that our Combine Model performs effectively and outperforms other algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Infographic: Social media statistics for 2012, http://www.digitalbuzzblog.com/social-media-statistics-stats-2012-infographic
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. In: PNAS (2002)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. (2004)
Nguyen, N.P., Dinh, T.N., Xuan, Y., Thai, M.T.: Adaptive algorithms for detecting community structure in dynamic social networks. In: INFOCOM (2011)
Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD (2006)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: KDD (2006)
Burt, R.: Structural Holes: The Social Structure of Competition. Harvard University Press (1992)
McKenzie Mohr, D., Smith, W.: Fostering Sustainable Behavior: An Introduction to Community Based Social Marketing. New Society Publishers (1971)
Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing. ACM Transactions on the Web (2007)
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD (2001)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD (2002)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)
Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale soical networks. Data Mining and Knowledge Discovery (2012)
Tang, S., Yuan, J., Mao, X., Li, X., Chen, W., Dai, G.: Relationship classification in large scale online social networks and its impact on information propagation. In: INFOCOM (2011)
Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: WSDM (2010)
Milgram, S.: The small world problem. Psychology Today (1967)
Shimp, T.A.: Advertising promotion: Supplemental aspects of integrated marketing communications. South-Western College Pub. (2002)
Blondel, V.D., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. (2008)
Hu, Y., Chen, H., Zhang, P., Di, Z., Li, M., Fan, Y.: Comparative definition of community and corresponding identifying algorithm. Phys. Rev. (2008)
Sun, T., Chen, W., Liu, Z., Wang, Y., Sun, X., Zhang, M., Lin, C.: Participation maximization based on social influence in online discussion forums. In: ICWSM (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bi, Y., Wu, W., Wang, L. (2013). Community Expansion in Social Network. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37487-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-37487-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37486-9
Online ISBN: 978-3-642-37487-6
eBook Packages: Computer ScienceComputer Science (R0)