192 research outputs found
Navigability of temporal networks in hyperbolic space
Information routing is one of the main tasks in many complex networks with a
communication function. Maps produced by embedding the networks in hyperbolic
space can assist this task enabling the implementation of efficient navigation
strategies. However, only static maps have been considered so far, while
navigation in more realistic situations, where the network structure may vary
in time, remain largely unexplored. Here, we analyze the navigability of real
networks by using greedy routing in hyperbolic space, where the nodes are
subject to a stochastic activation-inactivation dynamics. We find that such
dynamics enhances navigability with respect to the static case. Interestingly,
there exists an optimal intermediate activation value, which ensures the best
trade-off between the increase in the number of successful paths and a limited
growth of their length. Contrary to expectations, the enhanced navigability is
robust even when the most connected nodes inactivate with very high
probability. Finally, our results indicate that some real networks are
ultranavigable and remain highly navigable even if the network structure is
extremely unsteady. These findings have important implications for the design
and evaluation of efficient routing protocols that account for the temporal
nature of real complex networks.Comment: 10 pages, 4 figures. Includes Supplemental Informatio
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
The interconnected wealth of nations: Shock propagation on global trade-investment multiplex networks
The increasing integration of world economies, which organize in complex
multilayer networks of interactions, is one of the critical factors for the
global propagation of economic crises. We adopt the network science approach to
quantify shock propagation on the global trade-investment multiplex network. To
this aim, we propose a model that couples a Susceptible-Infected-Recovered
epidemic spreading dynamics, describing how economic distress propagates
between connected countries, with an internal contagion mechanism, describing
the spreading of such economic distress within a given country. At the local
level, we find that the interplay between trade and financial interactions
influences the vulnerabilities of countries to shocks. At the large scale, we
find a simple linear relation between the relative magnitude of a shock in a
country and its global impact on the whole economic system, albeit the strength
of internal contagion is country-dependent and the intercountry propagation
dynamics is non-linear. Interestingly, this systemic impact can be predicted on
the basis of intra-layer and inter-layer scale factors that we name network
multipliers, that are independent of the magnitude of the initial shock. Our
model sets-up a quantitative framework to stress-test the robustness of
individual countries and of the world economy to propagating crashes
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Model reproduces individual, group and collective dynamics of human contact networks
Empirical data on the dynamics of human face-to-face interactions across a variety of social venues have recently revealed a number of context-independent structural and temporal properties of human contact networks. This universality suggests that some basic mechanisms may be responsible for the unfolding of human interactions in the physical space. Here we discuss a simple model that reproduces the empirical distributions for the individual, group and collective dynamics of face-to-face contact networks. The model describes agents that move randomly in a two-dimensional space and tend to stop when meeting "attractive" peers, and reproduces accurately the empirical distributions
Prediction of new scientific collaborations through multiplex networks
The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks. © 2021, The Author(s)
Flocking-Enhanced social contagion
Populations of mobile agents animal groups, robot swarms, or crowds of people self-organize into a large diversity of states as a result of information exchanges with their surroundings. While in many situations of interest the motion of the agents is driven by the transmission of information from neighboring peers, previous modeling efforts have overlooked the feedback between motion and information spreading. Here we show that such a feedback results in contagion enhanced by flocking. We introduce a reference model in which agents carry an internal state whose dynamics is governed by the susceptible-infected-susceptible (SIS) epidemic process, characterizing the spread of information in the population and affecting the way they move in space. This feedback triggers flocking, which is able to foster social contagion by reducing the epidemic threshold with respect to the limit in which agents interact globally. The velocity of the agents controls both the epidemic threshold and the emergence of complex spatial structures, or swarms. By bridging together soft active matter physics and modeling of social dynamics, we shed light upon a positive feedback mechanism driving the self-organization of mobile agents in complex systems
Modeling echo chambers and polarization dynamics in social networks
Echo chambers and opinion polarization recently quantified in several
sociopolitical contexts and across different social media, raise concerns on
their potential impact on the spread of misinformation and on openness of
debates. Despite increasing efforts, the dynamics leading to the emergence of
these phenomena stay unclear. We propose a model that introduces the dynamics
of radicalization, as a reinforcing mechanism driving the evolution to extreme
opinions from moderate initial conditions. Inspired by empirical findings on
social interaction dynamics, we consider agents characterized by heterogeneous
activities and homophily. We show that the transition between a global
consensus and emerging radicalized states is mostly governed by social
influence and by the controversialness of the topic discussed. Compared with
empirical data of polarized debates on Twitter, the model qualitatively
reproduces the observed relation between users' engagement and opinions, as
well as opinion segregation in the interaction network. Our findings shed light
on the mechanisms that may lie at the core of the emergence of echo chambers
and polarization in social media
Emergence of polarized ideological opinions in multidimensional topic spaces
Opinion polarization is on the rise, causing concerns for the openness of
public debates. Additionally, extreme opinions on different topics often show
significant correlations. The dynamics leading to these polarized ideological
opinions pose a challenge: How can such correlations emerge, without assuming
them a priori in the individual preferences or in a preexisting social
structure? Here we propose a simple model that qualitatively reproduces
ideological opinion states found in survey data, even between rather unrelated,
but sufficiently controversial, topics. Inspired by skew coordinate systems
recently proposed in natural language processing models, we solidify these
intuitions in a formalism of opinions unfolding in a multidimensional space
where topics form a non-orthogonal basis. Opinions evolve according to the
social interactions among the agents, which are ruled by homophily: two agents
sharing similar opinions are more likely to interact. The model features phase
transitions between a global consensus, opinion polarization, and ideological
states. Interestingly, the ideological phase emerges by relaxing the assumption
of an orthogonal basis of the topic space, i.e. if topics thematically overlap.
Furthermore, we analytically and numerically show that these transitions are
driven by the controversialness of the topics discussed, the more controversial
the topics, the more likely are opinion to be correlated. Our findings shed
light upon the mechanisms driving the emergence of ideology in the formation of
opinions.Comment: 30 pages, 21 figure
Ceramic production and raw materials in the Tuscan-Ligurian region: an archaeological and petrographic approach in a diachronic perspective
This contribute focuses on the history of ceramic production of a large geographic area from the
archaeological point of view encompassing Liguria and N-W Tuscany and using a petro-archaeometrical approach ( essentially based on thin-section analyses of more than a thousand of samples)
A Markov model for inferring flows in directed contact networks
Directed contact networks (DCNs) are a particularly flexible and convenient
class of temporal networks, useful for modeling and analyzing the transfer of
discrete quantities in communications, transportation, epidemiology, etc.
Transfers modeled by contacts typically underlie flows that associate multiple
contacts based on their spatiotemporal relationships. To infer these flows, we
introduce a simple inhomogeneous Markov model associated to a DCN and show how
it can be effectively used for data reduction and anomaly detection through an
example of kernel-level information transfers within a computer.Comment: 12 page
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