scholarly journals Uncovering the structure and temporal dynamics of information propagation

2014 ◽  
Vol 2 (1) ◽  
pp. 26-65 ◽  
Author(s):  
MANUEL GOMEZ RODRIGUEZ ◽  
JURE LESKOVEC ◽  
DAVID BALDUZZI ◽  
BERNHARD SCHÖLKOPF
Keyword(s):  
News Media ◽  
Information Diffusion ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Inference Algorithm ◽  
Information Propagation ◽  
Transmission Rates ◽  
Diffusion Dynamics ◽  
Underlying Network ◽  
News Events

AbstractTime plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

scholarly journals Modeling and simulating the spatial spread of an epidemic through multiscale kinetic transport equations

2021 ◽  
pp. 1-39
Author(s):  
Walter Boscheri ◽  
Giacomo Dimarco ◽  
Lorenzo Pareschi
Keyword(s):  
Large Scale ◽  
Scaling Limit ◽  
Propagation Speed ◽  
Multiscale Model ◽  
Transport Equations ◽  
Small Scale ◽  
Spatial Spread ◽  
Model Interpretation ◽  
Mass Migration ◽  
Migration Dynamics

In this paper, we propose a novel space-dependent multiscale model for the spread of infectious diseases in a two-dimensional spatial context on realistic geographical scenarios. The model couples a system of kinetic transport equations describing a population of commuters moving on a large scale (extra-urban) with a system of diffusion equations characterizing the non-commuting population acting over a small scale (urban). The modeling approach permits to avoid unrealistic effects of traditional diffusion models in epidemiology, like infinite propagation speed on large scales and mass migration dynamics. A construction based on the transport formalism of kinetic theory allows to give a clear model interpretation to the interactions between infected and susceptible in compartmental space-dependent models. In addition, in a suitable scaling limit, our approach permits to couple the two populations through a consistent diffusion model acting at the urban scale. A discretization of the system based on finite volumes on unstructured grids, combined with an asymptotic preserving method in time, shows that the model is able to describe correctly the main features of the spatial expansion of an epidemic. An application to the initial spread of COVID-19 is finally presented.

Role of dynamic and thermodynamic processes for the propagation of organized convection in a large-scale flow

2020 ◽  
Author(s):  
Hyunju Jung ◽  
Ann Kristin Naumann ◽  
Bjorn Stevens
Keyword(s):  
Dynamic Process ◽  
Large Scale ◽  
Surface Wind ◽  
Propagation Speed ◽  
Background Wind ◽  
Near Surface ◽  
Large Scale Flow ◽  
Model Setup ◽  
Self Aggregation ◽  
Organized Convection

<p>Convective self-aggregation in radiative convective equilibrium has been studied due to its similarities to organized convection in the tropics. As tropical atmospheric phenomena are embedded in a large-scale flow, we impose a background wind to the model setup using convection-permitting simulation to analyze the interaction of convective self-aggregation with the background wind. The simulations show that when imposing a background wind, the convective cluster propagates in the direction of the imposed wind but slows down compared to what pure advection would suggest, and eventually becomes stationary. The dynamic process dominates slowing down the propagation speed of the cluster because the surface momentum flux acts as a drag on the near-surface wind, terminating the propagation. The thermodynamic process through the wind-induce surface feedback contributes to only 6% of the propagation speed of the convective cluster and is strongly modified by the dynamic process.</p>

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