![]() Torra, V., Jonsson, A., Navarro-Arribas, G., Salas, J.: Synthetic generation of spatial graphs. Nettleton, D.F.: Social Network Synthetic Data Generator (2021). MEDICI: A simple to use synthetic social network data generator. Nettleton, D.F., Nettleton, S., Canal i Farriol, M. ![]() Nettleton, D.F., Salas, J.: A data driven anonymization system for information rich online social network graphs. Nettleton, D.F.: A synthetic data generator for online social network graphs. & Apps, UPC, Barcelona, Spain, March 2015 Nettleton, D.F.: Generating synthetic online social network graph data and topologies. Robins, G., Pattison, P., Woolcock, J.: Small and other worlds: global network structures from local processes. In: Proceedings of the 2009 Winter Simulation Conference, 13–16 December 2009, pp.1003–1014 (2009)īoncz, P., et al.: Benchmark Design for Navigational Pattern Matching Benchmarking. In: Proceedings SocInfo 2014 (2014)īarrett, C.L., et al.: Generation and analysis of large synthetic social contact networks. Īli, A.M., Alvari, H., Hajibagheri, A., Lakkaraj, K., Sukthankar, G.: Synthetic generators for cloning social network data. Pérez-Rosés, H., Sebé, F.: Synthetic generation of social network data with endorsements. IEEE Access, 8, 130048–130065įeng, Z., et al.: A schema-driven synthetic knowledge graph generation approach with extended graph differential dependencies (GDDxs). R3MAT: A Rapid and Robust Graph Generator. IEEE, September 2017Īngles, R., Paredes, R., & García, R. In: 2017 IEEE High Performance Extreme Computing Conference (HPEC), pp. Samsi, S., et al.: Static graph challenge: Subgraph isomorphism. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. Park, H., Kim, M.S.: TrillionG: a trillion-scale synthetic graph generator using a recursive vector model. Tomašev, N., et al.: AI for social good: unlocking the opportunity for positive impact. Newman, N.: The costs of lost privacy: consumer harm and rising economic inequality in the age of Google. The ethics of Big Data: Balancing economic benefits and ethical questions of Big Data in the EU policy context”, Study of the European Economic and Social Committee (2017), Published by: “Visits and Publications” Unit EESC-2017–41-EN (2017) Nettleton, D.F.: Data mining of social networks represented as graphs. ![]() The system is made publicly available in a Github Java project. 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 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 main aim in this work is to implement an easy to use standalone end-user application which addresses the aforementioned issues. ![]() The main focus is the generation of realistic data, its assignment to and propagation within the graph. The issues to address are first to obtain a graph with a social network type structure, label it with communities. 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. ![]()
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