Abstract
The application of Agent-based Social Simulation (ABSS) for modeling social networks requires specific facilities for modeling, simulation and visualization of network structures. Moreover, ABSS can benefit from interactive shell facilities that can assist the model development process. We have addressed these problems through the development of a tool called SOIL, which provides a Python ABSS specifically designed for social networks. In this paper we present how this tool is applied to simulate viral marketing processes in a social network, and to evaluate the model with real data.
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Acknowledgements
This work is supported by the Spanish Ministry of Economy and Competitiveness under the R&D projects SEMOLA (TEC2015-68284-R) and EmoSpaces (RTC-2016–5053-7), by the Regional Government of Madrid through the project MOSI-AGIL-CM (grant P2013/ICE-3019, co-funded by EU Structural Funds FSE and FEDER), and by the European Union through the project MixedEmotions (Grant Agreement no: 141111). The authors want to thank Vahed Qazvinian for making available the rumor datasets for our research.
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Merino, E., Sánchez, J.M., García, D., Sánchez-Rada, J.F., Iglesias, C.A. (2017). Modeling Social Influence in Social Networks with SOIL, a Python Agent-Based Social Simulator. In: Demazeau, Y., Davidsson, P., Bajo, J., Vale, Z. (eds) Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection. PAAMS 2017. Lecture Notes in Computer Science(), vol 10349. Springer, Cham. https://doi.org/10.1007/978-3-319-59930-4_33
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DOI: https://doi.org/10.1007/978-3-319-59930-4_33
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