Abstract
Social networks have a great impact in our lives. While they started to improve and aid communication, nowadays they are used both in professional and personal spheres, and their popularity has made them attractive for developing a number of business models. Agent-based Social Simulation (ABSS) is one of the techniques that has been used for analysing and simulating social networks with the aim of understanding and even forecasting their dynamics. Nevertheless, most available ABSS platforms do not provide specific facilities for modelling, simulating and visualising social networks. This article aims at bridging this gap by introducing an ABSS platform specifically designed for modelling social networks. The main contributions of this paper are: (1) a review and characterisation of existing ABSS platforms; (2) the design of an ABSS platform for social network modelling and simulation; and (3) the development of a number of behaviour models for evaluating the platform for information, rumours and emotion propagation. Finally, the article is complemented by a free and open source simulator.
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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).
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Sánchez, J.M., Iglesias, C.A., Sánchez-Rada, J.F. (2017). Soil: An Agent-Based Social Simulator in Python for Modelling and Simulation of Social Networks. 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_19
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