References
- An, G., et al., 2009. Agent-based models in translational systems biology. Wiley Interdisciplinary Reviews Systems Biology & Medicine, 1 (2), 159–171. doi:10.1002/wsbm.45
- Bailey, N.T., 1975. The mathematical theory of infectious diseases and its applications. 5a Crendon Street, High Wycombe, Bucks HP13 6LE: Charles Griffin & Company Ltd.
- Bakshy, E., et al., 2012. The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web (WWW ‘12). New York, NY, USA: ACM, Vol. 8, 519–528.
- Barabási, A.L. and Albert, R., 1999. Emergence of scaling in random networks. Science, 286 (5439), 509–512.
- Bodin, Ö. and Crona, B.I., 2009. The role of social networks in natural resource governance: what relational patterns make a difference? Global Environmental Change, 19 (3), 366–374. doi:10.1016/j.gloenvcha.2009.05.002
- Boessen, A., et al., 2018. The built environment, spatial scale, and social networks: do land uses matter for personal network structure?. Environment and Planning B: Urban Analytics and City Science, 45 (3), 400–416.
- Centola, D., 2010. The spread of behavior in an online social network experiment. Science, 329 (5996), 1194–1197. doi:10.1126/science.1185231
- Christakis, N.A. and Fowler, J.H., 2007. The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357 (4), 370–379. doi:10.1056/NEJMsa066082
- Conover, M.D., et al., 2013. The geospatial characteristics of a social movement communication network. PloS one, 8 (3), e55957. doi:10.1371/journal.pone.0055957
- Dawkins, R., 1976. The selfish gene. New York: Oxford university press.
- Fortunato, S., Flammini, A., and Menczer, F., 2006. Scale-free network growth by ranking. Physical Review Letters, 96 (21), 218701. doi:10.1103/PhysRevLett.96.218701
- Gatti, M.A.D.C., et al., 2013. A simulation-based approach to analyze the information diffusion in microblogging online social network. In: Winter Simulations Conference. Washington, DC, USA: IEEE, 1685–1696.
- Goffman, W. and Newill, V.A., 1964. Generalization of epidemic theory. Nature, 204 (4955), 225–228. doi:10.1038/204225a0
- 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 Science Review, 9 (3), 1–18.
- Granovetter, M., 1978. Threshold models of collective behavior. American Journal of Sociology, 83 (6), 1420–1443. doi:10.1086/226707
- He, X.Y. and Hu, X.F., 2010. Modeling and simulation for agent-based information diffusion on worldwide web. Journal of System Simulation, 10, 2426–2431.
- Jansen, B.J., et al., 2009. Twitter power: tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60 (11), 2169–2188. doi:10.1002/asi.v60:11
- Jiang, B. and Yao, X., 2006. Location-based services and GIS in perspective. Computers, Environment and Urban Systems, 30 (6), 712–725. doi:10.1016/j.compenvurbsys.2006.02.003
- Karsai, I., Montano, E., and Schmickl, T., 2016. Bottom-up ecology: an agent-based model on the interactions between competition and predation. Letters in Biomathematics, 3 (1), 161–180.
- Kempe, D. and Kleinberg, J., 2003. Maximizing the spread of influence through a social network. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, D.C., USA: ACM. August, 137–146.
- Kleinberg, J.M., et al., 1999. July. The web as a graph: measurements, models, and methods. In: International Computing and Combinatorics Conference. Berlin, Heidelberg: Springer, 1–17.
- Lazer, D., et al., 2009. Life in the network: the coming age of computational social science. Science (New York, NY), 323 (5915), 721. doi:10.1126/science.1167742
- Lee, J. and Ye, X., 2018. An open source spatiotemporal model for simulating obesity prevalence. In: Thill, J., and Dragicevic S, eds. GeoComputational analysis and modeling of regional systems. Cham: Springer, 395–410.
- Liu, Q., Wang, Z., and Ye, X., 2018. Comparing mobility patterns between residents and visitors using geo-tagged social media data. Transactions in GIS, 22, 1372–1389. doi:10.1111/tgis.12478
- Livet, P., et al., 2010. Ontology, a mediator for agent-based modeling in social science. Journal of Artificial Societies & Social Simulation, 13 (1), 3. doi:10.18564/jasss.1538
- Luca, M., 2015. User-generated content and social media. In: Anderson, S. P., Waldfogel, J. and Stromberg, D., eds. Handbook of media economics. North-Holland, Vol. 1, 563–592.
- Matsuda, Y., Yamaguchi, K., and Nishioka, K., 2014. Discovery of spatio-temporal patterns from foursquare by diffusion-type estimation and ICA. In: Wermter, S. eds. Artificial Neural Networks and Machine Learning - ICANN 2014. Lecture Notes in Computer Science, Vol 8681. Springer, Cham.
- Mislove, A., et al., 2007. Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement. San Diego, USA: ACM. October, 29–42.
- Neri, F., 2004. Agent-based simulation of information diffusion in a virtual market place. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Beijing, China: IEEE. September, Vol.23, 333–336.
- Newman, M. E., 2008. The mathematics of networks. In: Palgrave Macmillan, eds., The new palgrave dictionary of economics. London: Palgrave Macmillan.
- Onnela, J.P., et al., 2011. Geographic constraints on social network groups. PLoS one, 6 (4), e16939. doi:10.1371/journal.pone.0016939
- Paul, M., and Dredze, M., 2011. You are what you Tweet: Analyzing twitter for public health. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain. pp. 265–272.
- Rapoport, A., 1953. Spread of information through a population with socio-structural bias: I. Assumption of Transitivity. The Bulletin of Mathematical Biophysics, 15 (4), 523–533. doi:10.1007/BF02476440
- Ratti, C., et al., 2006. Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33 (5), 727–748. doi:10.1068/b32047
- Romero, D.M., Meeder, B., and Kleinberg, J., 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, Hyderabad, India: ACM, 695–704.
- Scott, J., 2011. Social network analysis: Developments, advances, and prospects. Social Network Analysis & Mining, 1 (1), 21–26.
- Sharag-Eldin, A., Ye, X., and Spitzberg, B., 2018. Multilevel model of meme diffusion of fracking through twitter. Chinese Sociological Dialogue, 3, 17–43. doi:10.1177/2397200917752646
- Shaw, S., Tsou, M., and Ye, X., 2016. Human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 30 (9), 1687–1693. doi:10.1080/13658816.2016.1164317
- Sui, D. and Goodchild, M., 2011. The convergence of GIS and social media: challenges for GIScience. International Journal of Geographical Information Science, 25 (11), 1737–1748. doi:10.1080/13658816.2011.604636
- Sui, D.Z. and Goodchild, M.F., 2001. GIS as media? International Journal of Geographical Information Science, 15 (5), 387–390. doi:10.1080/13658810110038924
- Tsou, M.H. and Yang, J.A., 2016. Spatial social networks. In: D. Richardson, et al., eds.. the international encyclopedia of geography. Oxford, UK: John Wiley & Sons, Ltd. doi:10.1002/9781118786352.wbieg0904
- Tsou, M.-H., et al., 2013. Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US presidential election. Cartography and Geographic Information Science, 40 (4), 337–348. doi:10.1080/15230406.2013.799738
- Valente, T.W., 1996. Network models of the diffusion of innovations. Computational & Mathematical Organization Theory, 2 (2), 163–164. doi:10.1007/BF00240425
- Vespignani, A., 2009. Predicting the behavior of techno-social systems. Science, 325 (5939), 425–428. doi:10.1126/science.1171990
- Wang, F., et al., 2013, July. Characterizing information diffusion in online social networks with linear diffusive model. In: 2013 IEEE 33rd International Conference On Distributed Computing Systems (ICDCS). Philadelphia, PA, USA: IEEE, 307–316. doi:10.1016/j.cbpa.2012.10.019
- Wang, F., Wang, H., and Xu, K., 2012. Diffusive logistic model towards predicting information diffusion in online social networks. In: 2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCSW), Macau, China: IEEE. June, 133–139. doi:10.1177/1753193412451383
- Wang, Z., et al., 2018. A spatial econometric modeling of online social interactions using microblogs. Computers, Environment and Urban Systems, 70, 53–58. doi:10.1016/j.compenvurbsys.2018.02.001
- Wang, Z. and Ye, X., 2017. Social media analytics for natural disaster management. International Journal of Geographical Information Science. doi:10.1080/13658816.2017.1367003
- Wang, Z. and Ye, X., 2018. Space, time, and situational awareness in natural hazards: a case study of Hurricane Sandy with social media data. Cartography and Geographic Information Science, 1–13. doi:10.1080/15230406.2018.1483740
- Wasserman, S. and Faust, K., 1994. Social network analysis: methods and applications. Vol. 8. Cambridge: Cambridge university press.
- Watts, D.J., 2007. A twenty-first century science. Nature, 445 (7127), 489. doi:10.1038/445489a
- Ye, X., et al., 2018. Open source social network simulator focusing on spatial meme diffusion. In: S.-L. Shaw and D. Sui, eds. human dynamics research in smart and connected communities. Cham: Springer, 203–222.
- Ye, X. and Lee, J., 2016. Integrating geographic activity space and social network space to promote healthy lifestyles. ACM SIGSPATIAL Health GIS, 8 (1), 24–33.
- Ye, X. and Liu, X., 2018. Integrating social network and spatial analyses of the built environment. Environment and Planning B. doi:10.1177/2399808318772381
- Yin, H., et al., 2016. Discovering interpretable geo-social communities for user behavior prediction. In: IEEE, International Conference on Data Engineering. IEEE, 942–953.