arXiv subjects

Flavio Iannelli

Publications and source records attributed to Flavio Iannelli.

7 recordsLinked to original sources

arXiv2022

Bi-layer voter model: Modeling intolerant/tolerant positions and bots in opinion dynamics

The diffusion of opinions in Social Networks is a relevant process for adopting positions and attracting potential voters in political campaigns. Opinion polarization, bias, targeted diffusion, and the radicalization of postures are key elements for understanding voting dynamics. In particular, social bots are a new element that can have a pronounced effect on the formation of opinions during elections by, for instance, creating fake accounts in social networks to manipulate elections. Here we propose a voter model incorporating bots and radical or intolerant individuals in the decision-making process. The dynamics of the system occur in a multiplex network of interacting agents composed of two layers, one for the dynamics of opinions where agents choose between two possible alternatives, and the other for the tolerance dynamics, in which agents adopt one of two tolerance levels. The tolerance accounts for the likelihood to change opinion in an interaction, with tolerant (intolerant) agents switching opinion with probability $1.0$ ($γ\le 1$). We find that intolerance leads to a consensus of tolerant agents during an initial stage that scales as $τ^+ \sim γ^{-1} \ln N$, who then reach an opinion consensus during the second stage in a time that scales as $τ\sim N$, where $N$ is the number of agents. Therefore, very intolerant agents ($γ\ll 1$) could considerably slow down dynamics towards the final consensus state. We also find that the inclusion of a fraction $σ_{\mathbb{B}}^-$ of bots breaks the symmetry between both opinions, driving the system to a consensus of intolerant agents with the bots' opinion. Thus, bots eventually impose their opinion to the entire population, in a time that scales as $τ_B^- \sim γ^{-1}$ for $γ\ll σ_{\mathbb{B}}^-$ and $τ_B^- \sim 1/σ_{\mathbb{B}}^-$ for $σ_{\mathbb{B}}^- \ll γ$.

physics.soc-ph↗

arXiv2021

Stochastic modelling of blockchain consensus

Blockchain and general purpose distributed ledgers are foundational technologies which bring significant innovation in the infrastructures and other underpinnings of our socio-economic systems. These P2P technologies are able to securely diffuse information within and across networks, without need for trustees or central authorities to enforce consensus. In this contribution, we propose a minimalistic stochastic model to understand the dynamics of blockchain-based consensus. By leveraging on random-walk theory, we model block propagation delay on different network topologies and provide a classification of blockchain systems in terms of two emergent properties. Firstly, we identify two performing regimes: a functional regime corresponding to an optimal system function; and a non-functional regime characterised by a congested or branched state of sub-optimal blockchains. Secondly, we discover a phase transition during the emergence of consensus and numerically investigate the corresponding critical point. Our results provide important insights into the consensus mechanism and sub-optimal states in decentralised systems.

cs.DC↗

arXiv2018

Influencers identification in complex networks through reaction-diffusion dynamics

A pivotal idea in network science, marketing research and innovation diffusion theories is that a small group of nodes -- called influencers -- have the largest impact on social contagion and epidemic processes in networks. Despite the long-standing interest in the influencers identification problem in socio-economic and biological networks, there is not yet agreement on which is the best identification strategy. State-of-the-art strategies are typically based either on heuristic centrality metrics or on analytic arguments that only hold for specific network topologies or peculiar dynamical regimes. Here, we leverage the recently introduced random-walk effective distance -- a topological metric that estimates almost perfectly the arrival time of diffusive spreading processes on networks -- to introduce a new centrality metric which quantifies how close a node is to the other nodes. We show that the new centrality metric significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes. Our findings reveal the essential role of the network effective distance for the influencers identification and lead us closer to the optimal solution of the problem.

physics.soc-ph↗

arXiv2018

Cold denaturation of RNA secondary structures with loop entropy and quenched disorder

We study the folding of RNA secondary structures with quenched sequence randomness by means of the constrained annealing method. A thermodynamic phase transition is induced by including the conformational weight of loop structures. In addition to the expected melting at high temperature, a cold melting transition appears. Our results suggest that the cold denaturation of RNA found experimentally is, in fact, a continuous phase transition triggered by quenched sequence disorder. We calculate both hot and cold melting critical temperatures for the competing energy scenario between favorable and unfavorable base pairs and present a phase diagram as a function of the loop exponent and temperature.

physics.bio-ph↗

arXiv2018

Reaction-diffusion on random spatial networks with scale-free jumping rates via effective medium theory

We study epidemic processes using a metapopulation approach on the line featuring random transport rates between arbitrarily distant sites. An average transport network is found using a recently developed variant of the effective medium approximation (EMA) that is capable of dealing with these long-range connections. Using a Feynman-Kac argument in the effective medium, we derive an estimate on the size of the infected domain, and reproduce the known result of its exponential growth in time. We hereby demonstrate the applicability of long-range EMA to dynamical processes on networks more intricate than simple diffusion.

physics.soc-ph↗

arXiv2018

Political Discussion and Leanings on Twitter: the 2016 Italian Constitutional Referendum

The recent availability of large, high-resolution data sets of online human activity allowed for the study and characterization of the mechanisms shaping human interactions at an unprecedented level of accuracy. To this end, many efforts have been put forward to understand how people share and retrieve information when forging their opinion about a certain topic. Specifically, the detection of the political leaning of a person based on its online activity can support the forecasting of opinion trends in a given population. Here, we tackle this challenging task by combining complex networks theory and machine learning techniques. In particular, starting from a collection of more than 6 millions tweets, we characterize the structure and dynamics of the Italian online political debate about the constitutional referendum held in December 2016. We analyze the discussion pattern between different political communities and characterize the network of contacts therein. Moreover, we set up a procedure to infer the political leaning of Italian Twitter users, which allows us to accurately reconstruct the overall opinion trend given by official polls (Pearson's r=0.88) as well as to predict with good accuracy the final outcome of the referendum. Our study provides a large-scale examination of the Italian online political discussion through sentiment-analysis, thus setting a baseline for future studies on online political debate modeling.

physics.soc-ph↗

arXiv2017

Effective Distances for Epidemics Spreading on Complex Networks

We show that the recently introduced logarithmic metrics used to predict disease arrival times on complex networks are approximations of more general network-based measures derived from random walks theory. Using the daily air-traffic transportation data we perform numerical experiments to compare the infection arrival time with this alternative metric that is obtained by accounting for multiple walks instead of only the most probable path. The comparison with direct simulations of arrival times reveals a higher correlation compared to the shortest path approach used previously. In addition our method allows to connect fundamental observables in epidemic spreading with the cumulant generating function of the hitting time for a Markov chain. Our results provides a general and computationally efficient approach to the problem using only algebraic methods.

physics.soc-ph↗