Abstract
The motivation of the work in this paper is due to the need in research and applied fields for synthetic social network data due to (i) difficulties to obtain real data and (ii) data privacy issues of the real data. The issues to address are first to obtain a graph with a social network type structure, label it with communities. The main focus is the generation of realistic data, its assignment to and propagation within the graph. The main aim in this work is to implement an easy to use standalone end-user application which addresses the aforementioned issues. The methods used are the R-MAT and Louvain algorithms, with some modifications, for graph generation and community labeling respectively, and the development of a Java based system for the data generation using an original seed assignment algorithm followed by a second algorithm for weighted and probabilistic data propagation to neighbors and other nodes. The results show that a close fit can be achieved between the initial user specification and the generated data, and that the algorithms have potential for scale up. The system is made publicly available in a Github Java project.
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Nettleton, D.F., Nettleton, S., Farriol, M.C.i. (2021). MEDICI: A Simple to Use Synthetic Social Network Data Generator. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2021. Lecture Notes in Computer Science(), vol 12898. Springer, Cham. https://doi.org/10.1007/978-3-030-85529-1_22
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