The main focus of my research are human behavioral models for the analysis and design of online social networks. Specifically, I am investigating the role that Dunbar’s social circles play in how we interact online. While Dunbar’s social circles may not be known to you, Dunbar’s number might be. Recently, I have studied issues related to smart transportation, with a special focus on electric car sharing systems. In particular, I have designed optimized supply models for innovative car sharing systems and I have investigated the potential of mining car sharing datasets (hint: lots of very interesting info can be discovered). In the past, I have worked extensively on both the algorithmic aspects of opportunistic networks (contributing the HiBOp and ContentPlace protocols) and their analytical modelling. While opportunistic networks have never left the research labs to become mainstream, many ideas behind them have (e.g., computation at the Internet edge in 5G networks and D2D contact tracing for COVID-19). I have also worked on the modelization of human mobility (you may have heard of the HCMM protocol).
My h-index is 22 (source: Google Scholar).
PhD in Information Engineering, 2010
University of Pisa
MSc in Computer Engineering, 2006
University of Pisa
BSc in Computer Engineering, 2003
University of Pisa
New & noteworthy
Guest Editor for the Elsevier Pervasive and Mobile Computing Special Issue IEEE International Conference on Pervasive Computing and Communications (PerCom) 2021.
Lead Guest Editor for the Elsevier Pervasive and Mobile Computing Special Issue on IoT for Fighting COVID-19.
Editorial board member for Elsevier Computer Communications (since September 2018).
Editorial board member for Elsevier Pervasive and Mobile Computing (since September 2018).
Guest editor of the OSNEM CAOS 2019 Fast Track.
Guest editor of the Computer Communications CHANTS 2016 Fast Track.
Guest editor of the AOC 2011 Fast Track on Computer Communications.
TPC vice-chair for IEEE PerCom 2021 with Paul Castro (IBM Watson, USA) and Mahbub Hassan (University of New South Wales, Australia). The TPC chair is Amy L. Murphy (Fondazione Bruno Kessler, Italy).
IEEE SECON 2021.
ACM MobiHoc 2022.
The first MoD@ITSC17 workshop on Mobility-on-Demand systems, colocated with IEEE ITSC’17. My co-organisers were Raffaele Bruno, Francesco Ciari, Hironori Kato, Kara Kockelman.
IEEE CAOS 2020: The First IEEE INFOCOM Workshop on the Communications and Networking Aspects of Online Social Networks. TPC co-chair: Fabrizio Silvestri (Facebook).
IEEE CAOS 2019: The First IEEE INFOCOM Workshop on the Communications and Networking Aspects of Online Social Networks. TPC co-chair: Fabrizio Silvestri (Facebook).
ACM CHANTS 2016 (together with Marcelo Dias de Amorim from UPMC), IEEE AOC 2012 (together with Merkouris Karaliopoulos, from the University of Athens), IEEE AOC 2011 (together with Elena Pagani from the University of Milan).
AAAI ICWSM 2021, IEEE IPCCC 2021, IEEE SMARTCOMP 2021, IE 2021, HMB2020@SocInfo2020, AAAI ICWSM 2020, IEEE STINT 2020, IEEE IPCCC 2020, IE 2020, IEEE PerCom 2020, IE 2019, IEEE IPCCC 2019, ACM CHANTS 2019, IEEE PerCom 2019, IEEE PerCom 2019 WiP, ACM CHANTS 2018, IE 2018, IEEE LNC 2018, IEEE PerCom 2018, IEEE PerCom 2018 WiP, CoNEXT I-TENDER 2017, IFIP Networking 2017 (Poster and demo session), ACM CHANTS 2017, IEEE LNC 2017, VTC2017-Spring (Track: 1. Ad-Hoc, Mesh, M2M, and Sensor Networks), IEEE PerCom 2017, IEEE CoMoRea 2017, IEEE LNC 2016, IEEE PerCom 2016, IEEE PerCom Demos 2016, ICDCN’16, IEEE CoMoRea 2016, IEEE AOC 2016, ACM CHANTS’15, IEEE AOC 2015, IEEE CoMoRea 2015, IEEE PerMoby 2015, IEEE PerCom 2015, IEEE PerCom Ph.D. Forum 2015, IEEE ICDCN 2015, ACM CHANTS’14, IEEE ICCVE 2014, IEEE AOC 2014, IEEE PerCom 2014, IEEE PerMoby 2014, WON 2014, ACM CHANTS’13, IEEE ICCVE 2013, IEEE VTC’13, IEEE AOC’13, IEEE PerMoby’13, DySMoNet 2013, MiPS’13, ADHOCNETS’11, MNA’11, ACM MobiOpp 2010
I am co-supervising the following PhD students:
In the past, I have co-supervised:
I am not teaching any classes right now (we don’t have teaching obligations at CNR).
The cognitive constraints that humans exhibit in their social interactions have been extensively studied by anthropologists, who have highlighted their regularities across different types of social networks. We postulate that similar regularities can be found in other cognitive processes, such as those involving language production. In order to provide preliminary evidence for this claim, we analyse a dataset containing tweets of a heterogeneous group of Twitter users (regular users and professional writers). Leveraging a methodology similar to the one used to uncover the well-established social cognitive constraints, we find that a concentric layered structure (which we call ego network of words, in analogy to the ego network of social relationships) very well captures how individuals organise the words they use. The size of the layers in this structure regularly grows (approximately 2–3 times with respect to the previous one) when moving outwards, and the two penultimate external layers consistently account for approximately 60% and 30% of the used words (the outermost layer contains 100% of the words), irrespective of the number of the total number of layers of the user.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ personal data stores, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.
Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities. From the point of view of demand modelling, car sharing is a weak signal in the city landscape: only a small percentage of the population uses it, and thus it is difficult to study reliably with traditional techniques such as households travel diaries. In this work, we depart from these traditional approaches and we leverage web-based, digital records about vehicle availability in 10 European cities for one of the major active car sharing operators. We discuss which sociodemographic and urban activity indicators are associated with variations in car sharing demand, which forecasting approach (among the most popular in the related literature) is better suited to predict pickup and drop-off events, and how the spatio-temporal information about vehicle availability can be used to infer how different zones in a city are used by customers. We conclude the paper by presenting a direct application of the analysis of the dataset, aimed at identifying where to locate maintenance facilities within the car sharing operation area.
Ego networks have proved to be a valuable tool for understanding the relationships that individuals establish with their peers, both in offline and online social networks. Particularly interesting are the cognitive constraints associated with the interactions between the ego and the members of their ego network, whereby individuals cannot maintain meaningful interactions with more than 150 people, on average. In this work, we focus on the ego networks of journalists on Twitter, and we investigate whether they feature the same characteristics observed for other relevant classes of Twitter users, like politicians and generic users. Our findings are that journalists are generally more active and interact with more people than generic users. Their ego network structure is very aligned with reference models derived from the social brain hypothesis and observed in general human ego networks. Remarkably, the similarity is even higher than the one of politicians and generic users ego networks. This may imply a greater cognitive involvement with Twitter than with other social interaction means. Moreover, the ego networks of journalists are much stabler than those of politicians and generic users, and the ego-alter ties are often information-driven.
(as of Feb 22, 2018)
In short, sharing Tweet IDs (unlimited amount) as a research institution for non-commercial research should be fine according to the Twitter Developer Agreement and Policy.