NDlib was developed for research purposes.
So far it has been used/cited by the following publications:
- “NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks”
- G. Rossetti, L. Milli, S. Rinzivillo, A. Sirbu, D. Pedreschi, F. Giannotti. International Journal of Data Science and Analytics. 2017. DOI:0.1007/s41060-017-0086-6 (pre-print available on arXiv)
- “NDlib: Studying Network Diffusion Dynamics”
- G. Rossetti, L. Milli, S. Rinzivillo, A. Sirbu, D. Pedreschi, F. Giannotti. IEEE International Conference on Data Science and Advanced Analytics, DSAA. 2017.
- “Information Diffusion in Complex Networks: The Active/Passive Conundrum”
- L. Milli, G. Rossetti, D. Pedreschi, F. Giannotti International Conference on Complex Networks and their Applications, 2017. DOI:10.1007/978-3-319-72150-7_25
- “Active and passive diffusion processes in complex networks.”
- Milli, L., Rossetti, G., Pedreschi, D., & Giannotti, F. Applied network science, 3(1), 42, 2018.
- “Diffusive Phenomena in Dynamic Networks: a data-driven study”
- L. Milli, G. Rossetti, D. Pedreschi, F. Giannotti. 9th Conference on Complex Networks, CompleNet, 2018.
- “Stochastic dynamic programming heuristics for influence maximization–revenue optimization.”
- Lawrence, Trisha, and Patrick Hosein. International Journal of Data Science and Analytics (2018): 1-14.
- “Optimization of the Choice of Individuals to Be Immunized Through the Genetic Algorithm in the SIR Model”
- Rodrigues, R. F., da Silva, A. R., da Fonseca Vieira, V., AND Xavier, C. R. In International Conference on Computational Science and Its Applications (pp. 62-75), 2018.
- “Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model”
- Sîrbu, A., Pedreschi, D., Giannotti, F., & Kertész, J. PloS one, 14(3), 2019.
- “Similarity forces and recurrent components in human face-to-face interaction networks.”
- Flores, Marco Antonio Rodríguez, and Fragkiskos Papadopoulos. Physical review letters 121.25, 2018.
- “Resampling-based predictive simulation framework of stochastic diffusion model for identifying top-K influential nodes.”
- Ohara, K., Saito, K., Kimura, M., & Motoda, H. International Journal of Data Science and Analytics, 1-21, 2019.
- “Learning Data Mining.”
- Guidotti, R., Monreale, A., & Rinzivillo, S. (2018, October). In IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 361-370), 2018.