1,720,964 research outputs found
From the origin of life to pandemics: Emergent phenomena in complex systems
Get PDFWhen a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-Temporal scales might spontaneously arise. This non-Trivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems from physical to biological and social and is often related to collective behaviour. It is ubiquitous, from non-living entities such as oscillators that under specific conditions synchronize, to living ones, such as birds flocking or fish schooling. Despite the ample phenomenological evidence of the existence of systems emergent properties, central theoretical questions to the study of emergence remain unanswered, such as the lack of a widely accepted, rigorous definition of the phenomenon or the identification of the essential physical conditions that favour emergence. We offer here a general overview of the phenomenon of emergence and sketch current and future challenges on the topic. Our short review also serves as an introduction to the theme issue Emergent phenomena in complex physical and socio-Technical systems: from cells to societies, where we provide a synthesis of the contents tackled in the issue and outline how they relate to these challenges, spanning from current advances in our understanding on the origin of life to the large-scale propagation of infectious diseases.2022 The Author(s) Published by the Royal Society. All rights reserved
Percolation on feature-enriched interconnected systems
Get PDFPercolation is an emblematic model to assess the robustness of interconnected systems when some of their components are corrupted. It is usually investigated in simple scenarios, such as the removal of the system’s units in random order, or sequentially ordered by specific topological descriptors. However, in the vast majority of empirical applications, it is required to dismantle the network following more sophisticated protocols, for instance, by combining topological properties and non-topological node metadata. We propose a novel mathematical framework to fill this gap: networks are enriched with features and their nodes are removed according to the importance in the feature space. We consider features of different nature, from ones related to the network construction to ones related to dynamical processes such as epidemic spreading. Our framework not only provides a natural generalization of percolation but, more importantly, offers an accurate way to test the robustness of networks in realistic scenarios
Interplay between exogenous triggers and endogenous behavioral changes in contagion processes on social networks
No full textIn recent years, statistical physics’ methodologies have proven extremely successful in offering insights into the mechanisms that govern social interactions. However, the question of whether these models are able to capture trends observed in real-world datasets is hardly addressed in the current literature. With this work we aim at bridging the gap between theoretical modeling and validation with data. In particular, we propose a model for opinion dynamics on a social network in the presence of external triggers, framing the interpretation of the model in the context of misbehavior spreading. We divide our population in aware, unaware and zealot/educated agents. Individuals change their status according to two competing dynamics, referred to as behavioral dynamics and broadcasting. The former accounts for information spreading through contact among individuals whereas broadcasting plays the role of an external agent, modeling the effect of mainstream media outlets. Through both simulations and analytical computations we find that the stationary distribution of the fraction of unaware agents in the system undergoes a phase transition when an all-to-all approximation is considered. Surprisingly, such a phase transition disappears in the presence of a minimum fraction of educated agents. Finally, we validate our model using data collected from the public discussion on Twitter, including millions of posts, about the potential adverse effects of the AstraZeneca vaccine against COVID-19. We show that the intervention of external agents, as accounted for in our model, is able to reproduce some key features that are found in this real-world dataset
Non-Markovian random walks characterize network robustness to nonlocal cascades
No full textLocalized perturbations in a real-world network have the potential to trigger cascade failures at the whole system level, hindering its operations and functions. Standard approaches analytically tackling this problem are mostly based either on static descriptions, such as percolation, or on models where the failure evolves through first-neighbor connections, crucially failing to capture the nonlocal behavior typical of real cascades. We introduce a dynamical model that maps the failure propagation across the network to a self-avoiding random walk that, at each step, has a probability to perform nonlocal jumps toward operational systems' units. Despite the inherent non-Markovian nature of the process, we are able to characterize the critical behavior of the system out of equilibrium, as well as the stopping time distribution of the cascades. Our numerical experiments on synthetic and empirical biological and transportation networks are in excellent agreement with theoretical expectation, demonstrating the ability of our framework to quantify the vulnerability to nonlocal cascade failures of complex systems with interconnected structure
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Get PDFOnline social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to banning, or dismantling, strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs. unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms
Epidemic proximity and imitation dynamics drive infodemic waves during the COVID-19 pandemic
Get PDFAn infodemic - an outpouring of information, including misleading and also fake news - is accompanying the current pandemic caused by SARS-CoV-2. In the absence of valid therapeutic approaches, behavioral responses may seriously affect the social dynamics of contagion, so the infodemic may cause confusion and disorientation in the public, leading to possible individually and socially harmful choices. This new phenomenon requires specific modeling efforts to better understand the complex intertwining of the epidemic and infodemic components of a pandemic crisis, with a view to building an integrative public health approach. We propose three models, from epidemiology to game theory, as potential candidates for the onset of the infodemics and statistically assess their accuracy in reproducing real infodemic waves observed in a data set of 390 million tweets collected worldwide. Our results show that evolutionary game-theory models are the most suitable ones to reproduce the observed infodemic modulations around the onset of the local epidemic wave. Furthermore, we find that the number of confirmed COVID-19 reported cases in each country and worldwide are driving the modeling dynamics with opposite effects
Conceptualization of a functional random walker for the analysis of socio-technical systems
No full textThe Functional Resonance Analysis Method (FRAM) has been pointed out by several prior studies as an effective approach for modeling socio-technical systems. Despite previous contributions, FRAM still suffers from limited tractability in case of large models such as a difficulty of carrying out reliable and insightful analysis. This paper addresses this gap by proposing an approach inspired by network theory to enhance traditional FRAM analyses, namely the functional random walker (FRW). The definition of FRW complements the notion of random walks accounting for the variability being transferred inside the system from one function to another, offering a costeffective means of exploring FRAM instantiations. Five design criteria are presented to reproduce such analysis with any FRAM model, along with two indicators, namely the probability of visiting nodes and the mean passage time. These two metrics permit to study the FRAM model both at function level (how a single function behaves within the model) and at system level (how multiple functions interact with each other). A walk-through application of FRW is presented for a healthcare setting. This application revealed how the FRW sheds light on systems' strengths and vulnerabilities, and highlighted underlying knowledge, otherwise hidden in cluttered visual representations of FRAM instantiations
Multiscale statistical physics of the pan-viral interactome unravels the systemic nature of SARS-CoV-2 infections
Get PDFProtein–protein interaction networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID–19 and providing ground for applications, such as drug repurposing. Characterizing molecular (dis)similarities between SARS-CoV-2 and other viral agents allows one to exploit existing information about the alteration of key biological processes due to known viruses for predicting the potential effects of this new virus. Here, we compare the novel coronavirus network against 92 known viruses, from the perspective of statistical physics and computational biology. We show that regulatory spreading patterns, physical features and enriched biological pathways in targeted proteins lead, overall, to meaningful clusters of viruses which, across scales, provide complementary perspectives to better characterize SARS-CoV-2 and its effects on humans. Our results indicate that the virus responsible for COVID–19 exhibits expected similarities, such as to Influenza A and Human Respiratory Syncytial viruses, and unexpected ones with different infection types and from distant viral families, like HIV1 and Human Herpes virus. Taken together, our findings indicate that COVID–19 is a systemic disease with potential effects on the function of multiple organs and human body sub-systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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