Open Source Social Network Simulator Focusing on Spatial Meme Diffusion
It is crucial to discover, track, summarize, and even predict popular topics and events occurring in the social network in the space-time context. At the same time, it is very useful that a series of “what if” scenarios can be developed to estimate the meme diffusion. However, spatial social scientists have been slow to adopt and implement new methods for social media data analysis due to the lack of open source software packages, which become a major impediment to the promotion of human dynamics research. The availability and widespread use of source codes will play a critical role in the adoption of new perspectives and ideas enhancing spatial social network analytics. The proposed Open Source Social Network Simulator implements the methodological advances in an open source environment of Python for exploring spatial meme diffusion, using twitter data as the case study. The methods are built in open source environments and thus are easily extensible and customizable. The open source movement can also facilitate the explosion of the social media analytics routines by increasingly easier development processes with powerful scripting language environments.
This material is based upon work supported by the National Science Foundation under Grant No. 1416509 and 1637242.
- Bailey, N. (1975). The mathematical theory of infectious diseases and its applications. London: Charles Griffin & Company Ltd.Google Scholar
- Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012, April). The role of social networks in information diffusion. In Proceedings of the 21st International Conference on World Wide Web (pp. 519–528). ACM.Google Scholar
- Budak, C., Agrawal, D., & Abbadi, A. E. (2011). Limiting the spread of misinformation in social networks. In Proceedings of the 20th International Conference on World Wide Web (WWW ’11).Google Scholar
- Doo, M. (2012). Spatial and social diffusion of information and influence: Models and algorithms (Doctoral dissertation, Georgia Institute of Technology).Google Scholar
- Erdös, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5(17–61), 43.Google Scholar
- Guille, A., & Hacid. H. (2012). A predictive model for the temporal dynamics of information diffusion in online social networks. In WWW ’12 Companion (pp. 1145–1152).Google Scholar
- Hanneman, R. E. (2000). Introduction to social network methods. Online textbook supporting sociology 157. Riverside, CA: University of California.Google Scholar
- Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’03). Google Scholar
- Lee, J., & Ye, X. (2018). An open source spatiotemporal model for simulating obesity prevalence. In GeoComputational Analysis and Modeling of Regional Systems (pp. 395–410). Cham: Springer.Google Scholar
- Liang, Y., Caverlee, J., Cheng, Z., & Kamath, K. Y. (2013). How big is the crowd? Event and location based population modeling in social media. In Proceedings of the 24th ACM Conference on Hypertext and Hypermedia. Paris, France.Google Scholar
- Li, M., Ye, X., Zhang, S., Tang, X., & Shen, Z. (2017). A framework of comparative urban trajectory analysis. Environment and Planning B. https://doi.org/10.1177/2399808317710023.
- Morrill, R., Gaile, G. L., & Thrall, G. I. (1988). Spatial diffusion. SAGE scientific geography series 10. Newbury Park, CA: SAGE Publications, Inc.Google Scholar
- Newman, M. E. (2008). The mathematics of networks. The New Palgrave Encyclopedia of Economics, 2(2008), 1–12.Google Scholar
- Newman, M. E. J. (2003). The structure and function of complex networks. Society for Industrial and Applied Mathematics (SIAM) Review, 45(2), 167–256.Google Scholar
- Romero, D. M., Tan. C., & Ugander, J. (2013). On the interplay between social and topical structure. In Proceedings of AAAI International Conference on Weblogs and Social Media (pp. 516–525).Google Scholar
- Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011, March). Influence and passivity in social media. In Proceedings of the 20th international conference companion on World wide web (pp. 18–33). ACM.Google Scholar
- Scott, J. (2012). Social network analysis. UK: Sage.Google Scholar
- Wang, F., Wang, H., & Xu, K. (2012). Diffusive logistic model towards predicting information diffusion in online social networks. In ICDCS ’12 Workshops (pp. 133–139).Google Scholar
- Yang, J., & Counts, S. (2010). Predicting the speed, scale, and range of information diffusion in Twitter. In 4th International AAAI Conference on Weblogs and Social Media (ICWSM).Google Scholar
- Yang, J., & Leskovec, J. (2010). Modeling information diffusion in implicit networks. In ICDM ’10: IEEE International Conference On Data Mining, 2010.Google Scholar
- Ye, X. (2017). Open data and open source GIS, In Huang, B. (Ed.), Comprehensive geographic information systems (Vol. 1, pp. 42–49). Oxford: Elsevier. https://doi.org/10.1016/b978-0-12-409548-9.09592-0.
- Ye, X., Li, S., Yang, X., Lee, J., & Wu, L. (2018). The fear of Ebola: A tale of two cities in China. In Big data support of urban planning and management (pp. 113–132). Cham: Springer.Google Scholar
- Ye, X., & Lee, J. (2016). Integrating geographic activity space and social network space to promote healthy lifestyles. ACMSIGSPATIAL Health GIS., 8(1), 24–33.Google Scholar
- Zhang, J., Liu, X., & Tong, Z. (2012). Natural disaster risk assessment using information diffusion and geographical information system. In: Lu, J., Jain, L., & Zhang, G. (Eds.), Handbook on decision making (pp. 309–330). Berlin, Heidelberg: Springer.Google Scholar