I was a PhD student at the Computer Science Department of Gran Sasso Science Institute (GSSI) in L'Aquila, Italy. My thesis topic was on Adaptive Influence Maximization under the supervision of Gianlorenzo D'Angelo and Cosimo Vinci.
I was also a Postdoctoral Researcher at the Department of Environmental Sciences, Informatics and Statistics of Ca' Foscari University of Venice. I was working on the EUMEPLAT project with Fabiana Zollo.
I am passionate about data, food and travel. I love working with numbers, and occasionally take a break by researching about food. Sometimes, I contribute to RnD which is a blog about eating and traveling in Europe.
For research related queries, email me here. To have an unofficial chitchat send one here.
The influence maximization (IM) problem is a well known NP-hard problem of selecting the k most influential nodes in a network. Kempe et al. in their seminal work, formulized the IM problem as a discrete optimization problem. We call this the vanilla/non-adaptive IM problem, which will serve as the base for the adaptive version, studied by Golovin and Krause extensively in their paper.
The results for the IM problem is obtained by resorting to a greedy algorithm, which gives us a constant upper bound of 1-(1/e) following the results of Nemhauser et al.