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G. Rossetti, Morini, V., and Pollacci, L., “Capturing Political Polarization of Reddit Submissions in the Trump Era”, in SEBD, 2020.
G. Rossetti, “Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery”, in International Conference on Complex Networks and Their Applications, 2019.
G. Rossetti, Milli, L., and Cazabet, R., “CDLIB: a python library to extract, compare and evaluate communities from complex networks”, Applied Network Science, vol. 4, p. 52, 2019.
G. Rossetti, Citraro, S., and Milli, L., “Conformity: a Path-Aware Homophily measure for Node-Attributed Networks”, IEEE Intelligent SystemsIEEE Intelligent Systems, pp. 1 - 1, 2021.
G. Rossetti, Berlingerio, M., and Giannotti, F., “Scalable Link Prediction on Multidimensional Networks”, in ICDM Workshops, Vancouver, 2011, pp. 979-986.
G. Rossetti, Pappalardo, L., and Pedreschi, D., “Measuring tie strength in multidimensional networks”, in SEDB 2013, 2013.
G. Rossetti, Berlingerio, M., and Giannotti, F., “Link Prediction su Reti Multidimensionali”, in Sistemi Evoluti per Basi di Dati - {SEBD} 2011, Proceedings of the Nineteenth Italian Symposium on Advanced Database Systems, Maratea, Italy, June 26-29, 2011, 2011.
G. Rossetti, Guidotti, R., Pennacchioli, D., Pedreschi, D., and Giannotti, F., “Interaction Prediction in Dynamic Networks exploiting Community Discovery”, in International conference on Advances in Social Network Analysis and Mining, ASONAM 2015, Paris, France, 2015.
G. Rossetti, Pappalardo, L., Kikas, R., Pedreschi, D., Giannotti, F., and Dumas, M., “Community-centric analysis of user engagement in Skype social network”, in International conference on Advances in Social Network Analysis and Mining, Paris, France, 2015.
G. Rossetti, Pappalardo, L., and Rinzivillo, S., “A novel approach to evaluate community detection algorithms on ground truth”, in 7th Workshop on Complex Networks, Dijon, France, 2016.
G. Rossetti, Guidotti, R., Miliou, I., Pedreschi, D., and Giannotti, F., “A supervised approach for intra-/inter-community interaction prediction in dynamic social networks”, Social Network Analysis and Mining, vol. 6, p. 86, 2016.
G. Rossetti, Pappalardo, L., Pedreschi, D., and Giannotti, F., “Tiles: an online algorithm for community discovery in dynamic social networks”, Machine Learning, vol. 106, pp. 1213–1241, 2017.
G. Rossetti, Pappalardo, L., Kikas, R., Pedreschi, D., Giannotti, F., and Dumas, M., “Homophilic network decomposition: a community-centric analysis of online social services”, Social Network Analysis and Mining, vol. 6, p. 103, 2016.
G. Rossetti and Cazabet, R., “Community Discovery in Dynamic Networks: a Survey”, Journal ACM Computing Surveys, vol. 51, 2018.
G. Rossetti, Milli, L., Giannotti, F., and Pedreschi, D., “Forecasting success via early adoptions analysis: A data-driven study”, PloS one, vol. 12, p. e0189096, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, pp. 1–19, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: Studying Network Diffusion Dynamics”, in IEEE International Conference on Data Science and Advanced Analytics, DSA, Tokyo, 2017.
G. Rossetti, Pedreschi, D., and Giannotti, F., “Node-centric Community Discovery: From static to dynamic social network analysis”, Online Social Networks and Media, vol. 3, pp. 32–48, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, vol. 5, pp. 61–79, 2018.
G. Rossetti, Citraro, S., and Milli, L., “Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks”, arXiv preprint arXiv:2012.05195, 2020.
G. Rossetti, Milli, L., Citraro, S., and Morini, V., “UTLDR: an agent-based framework for modeling infectious diseases and public interventions”, arXiv preprint arXiv:2011.05606, 2020.
G. Rossetti, “ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks”, Applied Network Science, vol. 5, pp. 1–23, 2020.