Abstract
Simulations can be used for the study of Online Social Networks (OSNs) as a means to harness their size and complexity, and to overcome the difficulties to set up experiments in real environments. Most existing tools for the analysis of OSNs focus on the graph structure of networks and emulate changes only from statistical data. This approach is unsuitable to study the evolution of OSNs as a consequence of the personal attributes and behaviours of their members. Our work addresses this issue with an agent-based simulation framework for OSNs called Krowdix. It provides support to specify discrete time simulations, where agents represent members of OSNs acting according to their profiles and context. This context comprehends the environment, other agents, groups of agents, and the whole network. Agent actions are responsible of network changes. Additionally, system actions can represent unexpected events external to agents. This agent-based approach facilitates the translation of actual observations to simulation models, and explaining networks in terms of their members.
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Blanco-Moreno, D., Cárdenas, M., Fuentes-Fernández, R., Pavón, J. (2014). Krowdix: Agent-Based Simulation of Online Social Networks. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_47
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DOI: https://doi.org/10.1007/978-3-319-12027-0_47
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