Modeling Memory Effects in Activity-Driven Networks

Zino, Lorenzo; Rizzo, Alessandro; Porfiri, Maurizio

doi:10.1137/18m1171485

Cited by 40 publications

(31 citation statements)

References 86 publications

(151 reference statements)

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“…The distribution of this parameter, called activity, can be inferred from real-world data [14]. The potential of ADNs has been demonstrated through the study of several network problems, including epidemics [15][16][17][18][19], diffusion of innovation [20], opinion formation [21], and percolation [22].…”

Section: Introductionmentioning

confidence: 99%

Backbone reconstruction in temporal networks from epidemic data

et al. 2019

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Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.

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Section: Introductionmentioning

confidence: 99%

Backbone reconstruction in temporal networks from epidemic data

et al. 2019

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“…Moreover, to better simulate the dynamic nature of social interaction, we assume that not all social ties of a given node are active during each simulation iteration. To implement such a constraint, we leverage a simple Activity Driven (Perra et al 2012 ) network model, a framework often employed to simulate evolutive dynamics of network topology in the absence of explicit temporal interaction data (Liu et al 2014 ; Pozzana et al 2017 ; Zino et al 2018 ; Ogura et al 2019 ). Each agent v ∈ V in the network has assigned an activation probability a v ∈ [0,1] identifying the percentage of edges (chosen uniformly at random) he activates during each simulation iteration.…”

Section: Extending Utldr: Agent-based Modeling and Human Mobilitymentioning

confidence: 99%

UTLDR: an agent-based framework for modeling infectious diseases and public interventions

et al. 2021

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Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate “what if” epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.

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“…We investigate the presence of such a phase transition by means of Monte Carlo numerical simulations, following a method similar to the ones proposed to numerically estimate the epidemic threshold in epidemic models 54,55 . Specifically, we run repeated independent simulations of the process for different values of commitment λ , keeping track of the fraction of adopters of the innovation in each run at the end of a fixed observation window of duration T , which is equal to ( x(T ) +1)/2.…”

Section: A Opinions Not Directly Influenced By Actionsmentioning

confidence: 99%

A two-layer model for coevolving opinion dynamics and collective decision-making in complex social systems

Zino

Cao

2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Motivated by the literature on opinion dynamics and evolutionary game theory, we propose a novel mathematical framework to model the intertwined coevolution of opinions and decision-making in a complex social system. In the proposed framework, the members of a social community update their opinions and revise their actions as they learn of others' opinions shared on a communication channel, and observe of others' actions through an influence channel; these interactions determine a two-layer network structure. We offer an application of the proposed framework by tailoring it to study the adoption of a novel social norm, demonstrating that the model is able to capture the emergence of several real-world collective phenomena such as paradigm shifts and unpopular norms. Through the establishment of analytical conditions and Monte Carlo numerical simulations, we shed light on the role of the coupling between opinion dynamics and decision-making, and of the network structure, in shaping the emergence of complex collective behavior in social systems.

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Modeling Memory Effects in Activity-Driven Networks

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Cited by 40 publications

References 86 publications

Backbone reconstruction in temporal networks from epidemic data

Backbone reconstruction in temporal networks from epidemic data

UTLDR: an agent-based framework for modeling infectious diseases and public interventions

A two-layer model for coevolving opinion dynamics and collective decision-making in complex social systems

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