Bibliography

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.