# Seeking powerful information initial spreaders in online social networks: a dense group perspective

- 96 Downloads
- 1 Citations

## Abstract

The rapid growth of online social networks (OSNs) has ultimately facilitated information spreading and changed the economics of mobile networks. It is important to understand how to spread information as widely as possible. In this paper, we aim to seek powerful information initial spreaders with an efficient manner. We use the mean-field theory to characterize the process of information spreading based on the Susceptible Infected (SI) model and validate that the prevalence of information depends on the network density. Inspired by this result, we seek the initial spreaders from closely integrated groups of nodes, *i.e.*, dense groups (DGs). In OSNs, DGs distribute dispersedly over the network, so our approach can be fulfilled in a distributed way by seeking the spreaders in each DG. We first design a DG Generating Algorithm to detect DGs, where nodes within the DG have more internal connections than external ones. Second, based on the detected DGs, we design a criterion to seek powerful initial spreaders from each DG. We conduct experiments as well as statistical analysis on real OSNs. The results show that our approach provides a satisfactory performance as well as computational efficiency.

### Keywords

Online social networks Information initial spreader Dense group Epidemic model### References

- 1.Ma, L., Ma, C., & Zhang, H. (2016). Identifying influential spreaders in complex networks based on gravity formula.
*Physica A: Statistical Mechanics and its Applications,**451*, 205–212.CrossRefGoogle Scholar - 2.Zhong, L., Liu, J., & Shang, M. (2015). Iterative resource allocation based on propagation feature of node for identifying the influential nodes.
*Physics Letters A*,*379*(38), 2272–2276.CrossRefGoogle Scholar - 3.Ren, Z., Zeng, A., Chen, D., Liao, H., & Liu, J. (2014). Iterative resource allocation for ranking spreaders in complex networks.
*Europhysics Letters*,*106*(4), 48005.CrossRefGoogle Scholar - 4.Horel, T., & Singer, Y. (2015). Scalable methods for adaptively seeding a social network. In
*Proceedings WWW*, Florence, Italy.Google Scholar - 5.Chen, W., Wang, Y., & Yang, S. (2009). Efficient influence maximization in social networks. In
*Proceedings ACM SIGKDD*, Paris, France.Google Scholar - 6.Chen, W., Lu, W., & Zhang, N. (2012). Time-critical influence maximization in social networks with time-delayed diffusion process. In
*Proceedings AAAI*, Toronto, Canada.Google Scholar - 7.Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the spread of influence through a social network. In
*Proceedings ACM SIGKDD*, Washington, DC.Google Scholar - 8.Neglia, G. N., Ye, X., Gabielkov, M., & Legout, A. (2014). How to network in online social networks. In
*Proceedings NetSciCom*, Toronto, Canada.Google Scholar - 9.Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society.
*Nature*,*435*(7043), 814–818.CrossRefGoogle Scholar - 10.Gregory, S. (2010). Finding overlapping communities in networks by label propagation.
*New Journal of Physics*,*12*(10), 1–26.CrossRefGoogle Scholar - 11.Nguyen, N. P., Dinh, T. N., Tokala, S., & Thai, M. T. (2011). Overlapping communities in dynamic networks: Their detection and mobile applications. In
*ACM MOBICOM*, Las Vegas, NV.Google Scholar - 12.Benaim, M., & Le Boudec, J.-Y. (2008). A class of mean field interaction models for computer and communication systems.
*Performance Evaluation*,*65*(11), 823–838.CrossRefGoogle Scholar - 13.Le Boudec, J. -Y., McDonald, D., & Mundinger, J. (2007). A generic mean field convergence result for systems of interacting objects. In
*Proceedings QEST*, Edinburgh, Scotland.Google Scholar - 14.Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is twitter, a social network or a news media?. In
*Proceedings WWW*, Raleigh, NC.Google Scholar - 15.Guo, Z., Li, Z., & Tu, H. (2011). Sina microblog: An information-driven online social network. In
*Proceedings CW*, Banff, Canada.Google Scholar - 16.Yang, F., Liu, Y., Yu, X., & Yang, M. (2012). Automatic detection of rumor on sina weibo. In
*Proceedings ACM SIGKDD*, Beijing, China.Google Scholar - 17.Fan, J., Chen, J., Du, Y., Gao, W., Wu, J., & Sun, Y. (2013). Geocommunity-based broadcasting for data dissemination in mobile social networks.
*IEEE TPDS*,*24*(4), 734–743.Google Scholar - 18.Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In
*Proceedings WWW*, Lyon, France.Google Scholar - 19.Miritello, G., Moro, E., & Lara, R. (2011). Dynamical strength of social ties in information spreading.
*Physical Review E*,*83*(4), 045102.CrossRefGoogle Scholar - 20.Eugster, P. T., Guerraoui, R., Kermarrec, A.-M., & Massoulié, L. (2004). Epidemic information dissemination in distributed systems.
*Computer*,*37*(5), 60–67.CrossRefGoogle Scholar - 21.Khelil, A., Becker, C., Tian, J., & Rothermel, K. (2002). An epidemic model for information diffusion in manets. In
*Proceedings ACM MSWiM*, Atlanta, GA.Google Scholar - 22.Guille, A., & Hacid, H. (2012). A predictive model for the temporal dynamics of information diffusion in online social networks. In
*Proceedings WWW*, Lyon, France.Google Scholar - 23.Gopalan, A., Banerjee, S., Das, A. K., & Shakkottai, S. Random mobility and the spread of infection. In
*Proceedings IEEE INFOCOM*.Google Scholar - 24.Banerjee, S., Gopalan, A., Das, A. K., & Shakkottai, S. (2014). Epidemic spreading with external agents.
*IEEE Transactions on Information Theory*,*60*(7), 4125–4138.MathSciNetCrossRefGoogle Scholar - 25.Ganesh, A., Massoulié, L., & Towsley, D. (2005). The effect of network topology on the spread of epidemics. In
*Proceedings INFOCOM*, Miami, FL.Google Scholar - 26.Zhu, K., & Ying, L. (2016). Information source detection in the sir model: A sample-path-based approach.
*IEEE/ACM ToNransactions on Networking*,*24*(1), 408–421.CrossRefGoogle Scholar - 27.Fortunato, S. (2010). Community detection in graphs.
*Physics Reports*,*486*(3), 75–174.MathSciNetCrossRefGoogle Scholar - 28.Alba, R. D. (1973). A graph-theoretic definition of a sociometric clique.
*Journal of Mathematical Sociology*,*3*(1), 113–126.MathSciNetCrossRefMATHGoogle Scholar - 29.Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks.
*Physical Review E*,*76*(3), 1–11.CrossRefGoogle Scholar - 30.Morone, F., & Makse, H. A. (2015). Influence maximization in complex networks through optimal percolation.
*Nature*,*524*(7563), 65–68.CrossRefGoogle Scholar - 31.Dinh, T. N., Zhang, H., Nguyen, D. T., & Thai, M. T. (2014). Cost-effective viral marketing for time-critical campaigns in large-scale social networks.
*IEEE/ACM ToN*,*22*(6), 2001–2011.CrossRefGoogle Scholar - 32.Ok, J., Jin, Y., Shin, J., & Yi, Y. (2014). On maximizing diffusion speed in social networks: Impact of random seeding and clustering. In
*Proceedings ACM SIGMETRICS*, Austin, TX.Google Scholar - 33.Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks.
*Reviews of Modern Physics*,*74*(1), 47.MathSciNetCrossRefMATHGoogle Scholar - 34.Boguná, M., & Pastor-Satorras, R. (2002). Epidemic spreading in correlated complex networks.
*Physical Review E*,*66*(4), 047104.CrossRefGoogle Scholar - 35.Wang, S., Zhou, X., Wang, Z., & Zhang, M. (2012). Please spread: Recommending tweets for retweeting with implicit feedback. In
*Proceedings DUBMMSM*Maui, HI.Google Scholar - 36.Osborne, M . J. (2004).
*An introduction to game theory. vol. 3, no. 3*. New York: Oxford University Press.Google Scholar - 37.Sina Corp. Sina Weibo API. Available at http://open.weibo.com/.
- 38.Leskovec, J. Stanford large network dataset collection. Available at http://snap.stanford.edu/data/.