Giulio Cimini

Associate Professor of Theoretical Physics
University of Rome Tor Vergata

TwitterLinkedInLinkEmail

About me

I am Associate Professor of Theoretical Physics at the Physics Department of University of Rome Tor Vergata, and Research Associate at the "Enrico Fermi" research center

I am a statistical physicist with research interests in complex networks theory and interdisciplinary socio-economic applications. I got my PhD in Theoretical and Interdisciplinary Physics in the group of Yi-Cheng Zhang at University of Fribourg. Before my current position, I worked with Anxo Sánchez at Universidad Carlos III in Madrid as SNSF fellow, in the PIL group of Luciano Pietronero and Andrea Gabrielli at ISC-CNR in Rome, and in the NETWORKS unit at IMT Lucca with Guido Caldarelli. 

I serve in the board of the Network Science Society, the council of the Complex Systems Society and the steering committee of CCS/Italy. I am Associate Editor of Frontiers in Physics - Interdisciplinary Physics.

My current research topics are:

News

New Preprint on arXiv

A. Desiderio, L. M. Aiello, G. Cimini, L. Alessandretti. The causal role of the Reddit collective action on the GameStop short squeeze. arXiv:2401.14999 (2024)

In early 2021, the stock prices of GameStop, AMC, Nokia, and BlackBerry experienced dramatic increases, triggered by short squeeze operations that have been largely attributed to Reddit's retail investors. These events showcased, for the first time, the potential of online social networks to catalyze financial collective action. How, when and to what extent Reddit users played a causal role in driving up these prices, however, remains unclear. To address these questions, we employ causal inference techniques, leveraging data capturing activity on Reddit and Twitter, and trading volume with a high temporal resolution. We find that Reddit discussions foreshadowed trading volume before the GameStop short squeeze, with their predictive power being particularly strong on hourly time scales. This effect emerged abruptly and became prominent a few weeks before the event, but waned once the community of investors gained widespread visibility through Twitter. As the causal link unfolded, the collective investment of the Reddit community, quantified through each user's financial position on GameStop, closely mirrored the market capitalization of the stock. The evidence from our study suggests that Reddit users fueled the GameStop short squeeze, and thereby Reddit served as a coordination hub for a shared financial strategy. Towards the end of January, users talking about GameStop contributed to raise the popularity of BlackBerry, AMC and Nokia, which emerged as the most popular stocks as the community gained global recognition. Overall, our results shed light on the dynamics behind the first large-scale financial collective action driven by social media users.

New Preprint on arXiv

G. Palermo, A. Mancini, A. Desiderio, R. Di Clemente, G. Cimini. Spontaneous opinion swings in the Voter Model with latency. arXiv:2311.10045

The cognitive process of opinion formation is often characterized by stubbornness or resistance of agents to changes of opinion. To capture such a feature we introduce a constant latency time in the standard voter model of opinion dynamics: after switching opinion, an agent must keep it for a while. This seemingly simple modification drastically changes the stochastic diffusive behavior of the original model, leading to deterministic dynamical oscillations in the average opinion of the agents. We explain the origin of the oscillations and develop a mathematical formulation of the dynamics that is confirmed by extensive numerical simulations. We further characterize the rich phase space of the model and its asymptotic behavior. Our work offers insights into understanding and modeling opinion swings in diverse social contexts.

New Preprint on arXiv

G. Tsekenis, G. Cimini, M. Kalafatis, A. Giacometti, T. Gili, G. Caldarelli. Network topology mapping of Chemical Compounds Space. arXiv:2402.11774

We define bipartite and monopartite relational networks of chemical elements and compounds using two different datasets of inorganic chemical and material compounds, as well as study their topology. We discover that the connectivity between elements and compounds is distributed exponentially for materials, and with a fat tail for chemicals. Compounds networks show similar distribution of degrees, and feature a highly-connected club due to oxygen. Chemical compounds networks appear more modular than material ones, while the communities detected reveal different dominant elements specific to the topology. We successfully reproduce the connectivity of the empirical chemicals and materials networks by using a family of fitness models, where the fitness values are derived from the abundances of the elements in the aggregate compound data. Our results pave the way towards a relational network-based understanding of the inherent complexity of the vast chemical knowledge atlas, and our methodology can be applied to other systems with the ingredient-composite structure.

New Preprint on arXiv

A. Desiderio, A. Mancini, G. Cimini, R. Di Clemente. Recurring patterns in online social media interactions during highly engaging events. arXiv:2306.14735

People nowadays express their opinions in online spaces, using different forms of interactions such as posting, sharing and discussing with one another. These digital traces allow to capture how people dynamically react to the myriad of events occurring in the world. By unfolding the structure of Reddit conversations, we describe how highly engaging events happening in the society affect user interactions and behaviour with respect to unperturbed discussion patterns. Conversations, defined as a post and the comments underneath, are analysed along their temporal and semantic dimensions. We disclose that changes in the pace and language used in conversations exhibit notable similarities across diverse events. Conversations tend to become repetitive with a more limited vocabulary, display different semantic structures and feature heightened emotions. As the event approaches, the shifts occurring in conversations are reflected in the users' dynamics. Users become more active and they exchange information with a growing audience, despite using a less rich vocabulary and repetitive messages. The peers of each user fill up more semantic space, shifting the dialogue and widening the exchange of information. The recurring patterns we discovered are persistent across several contexts, thus represent a fingerprint of human behavior, which could impact the modeling of online social networks interactions.

New Paper on Chaos, Solitons & Fractals

M. Fessina, A. Zaccaria, G. Cimini, T. Squartini. Pattern-detection in the global automotive industry: a manufacturer-supplier-product network analysis. Chaos, Solitons & Fractals 181, 114630 (2024)

Production networks arise from supply and customer relations among firms. These systems are gaining growing attention as a consequence of disruptions due to natural or man-made disasters that happened in the last years, such as the Covid-19 pandemic or the Russia-Ukraine war. However, data constraints force the few, available studies to consider only country-specific production networks. In order to fully capture the cross-country structure of modern supply chains, here we focus on the global automotive industry as represented by the MarkLines Automotive dataset. After representing this data as a network of manufacturers, suppliers, and products, we perform a pattern-detection exercise using a statistically grounded validation technique based on the maximum entropy principle. We reveal the presence of a significantly large number of V-shaped and square-shaped motifs, indicating that manufacturing firms compete and are seldom engaged in a buyer-supplier relationship, while they typically have many suppliers in common. Interestingly, generalist and specialist suppliers coexist in the network. Additionally, we unveil the presence of geographical patterns, with manufacturers clustering around groups of suppliers; for instance, Chinese firms constitute a disconnected community, likely an effect of the protectionist policies promoted by the Chinese government. We also show the tendency of suppliers to organize their production by targeting specific functional modules of a vehicle. Besides shedding light on the self-organising principles shaping production networks, our findings open up the possibility of designing realistic generative models of supply chains, to be used for testing the resilience of the interconnected global economy.

New Paper on Physica A

G. Ricciardi, G. Montagna, G. Caldarelli, G. Cimini. Dimensional reduction of solvency contagion dynamics on financial networks. Physica A 630, 129287 (2023)

Modelling systems with networks has been a powerful approach to tame the complexity of several phenomena. Unfortunately, the large number of variables to take into consideration often makes concrete problems difficult to handle. Methods of dimensional reduction are useful tools to rescale a complex network down to a low-dimensional effective system and thus to capture its global dynamical features. Here we study the application of the degree-weighted and spectral reduction methods to an important class of dynamical processes on networks: the propagation of credit shocks within an interbank network, modelled according to the DebtRank algorithm. We introduce an effective version of the dynamics, characterized by functions with continuous derivatives that can be handled by the dimensional reduction. We test the reduction methods against the full dynamical system in different interbank market settings: homogeneous and heterogeneous networks generated from state-of-the-art reconstruction methods as well as networks derived from empirical e-MID data. Our results indicate that, for proper choices of the bank default probability, reduction methods can provide reliable estimates of systemic risk in the market, with the spectral reduction better handling heterogeneous networks. Finally, we provide new insights on the nature and working principles of dimensional reduction methods.

New Paper on Journal of Informetrics

A. Patelli, L. Napolitano, G. Cimini, A. Gabrielli. Geography of science: Competitiveness and inequality. Journal of Informetrics 17(1): 101357 (2023)

We characterize the temporal dynamics of Scientific Fitness, as defined by the Economic Fitness and Complexity (EFC) framework, and R&D expenditures at the geographic scale of nations. Our analysis highlights common patterns across similar research systems, and shows how developing nations (China in particular) are quickly catching up with the developed world. This paints the picture of a general growth of scientific and technical capabilities of nations induced by the spreading of information typical of the scientific environment. Shifting the focus of the analysis to the regional level, we find that even developed nations display a considerable level of inequality in the Scientific Fitness of their internal regions. Further, we assess comparatively how the competitiveness of each geographic region is distributed over the spectrum of research sectors. Overall, the Scientific Fitness represents the first high quality estimation of the scientific strength of nations and regions, opening new policy-making applications for better allocating resources, filling inequality gaps and ultimately promoting innovation.

PRIN(s) 2022 and 2022 PNRR

The projects with acronym RENet (in collaboration with Tiziano Squartini) and C2T (with Tiziano Squartini, Diego Garlaschelli and Andrea Gabrielli) have been funded by the Italian Ministry of University under the PRIN programme. Stay tuned!

Highlights

MSc in Complex Systems @Tor Vergata