Journal ArticleDOI
Information cascades in complex networks
Mahdi Jalili,Matjaž Perc +1 more
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TLDR
Simulation results on sample networks reveal just how relevant the centrality of initiator nodes is on the latter development of an information cascade, and the spreading influence of a node is defined as the fraction of nodes that is activated as a result of the initial activation of that node.Abstract:
Information cascades are important dynamical processes in complex networks. An information cascade can describe the spreading dynamics of rumour, disease, memes, or marketing campaigns, which initially start from a node or a set of nodes in the network. If conditions are right, information cascades rapidly encompass large parts of the network, thus leading to epidemics or epidemic spreading. Certain network topologies are particularly conducive to epidemics, while others decelerate and even prohibit rapid information spreading. Here we review models that describe information cascades in complex networks, with an emphasis on the role and consequences of node centrality. In particular, we present simulation results on sample networks that reveal just how relevant the centrality of initiator nodes is on the latter development of an information cascade, and we define the spreading influence of a node as the fraction of nodes that is activated as a result of the initial activation of that node. A systemic review of existing results shows that some centrality measures, such as the degree and betweenness, are positively correlated with the spreading influence, while other centrality measures, such as eccentricity and the information index, have negative correlation. A positive correlation implies that choosing a node with the highest centrality value will activate the largest number of nodes, while a negative correlation implies that the node with the lowest centrality value will have the same effect.We discuss possible applications of these results, and we emphasize how information cascades can help us identify nodes with the highest spreading capability in complex networks.read more
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The echo chamber effect on social media
Matteo Cinelli,Gianmarco De Francisci Morales,Alessandro Galeazzi,Walter Quattrociocchi,Michele Starnini +4 more
TL;DR: In this article, a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter was performed.
Journal ArticleDOI
Network science of biological systems at different scales: A review
Marko Gosak,Rene Markovič,Jurij Dolenšek,Marjan Slak Rupnik,Marko Marhl,Andraž Stožer,Matjaž Perc +6 more
TL;DR: This work presents research highlights ranging from determination of the molecular interaction network within a cell to studies of architectural and functional properties of brain networks and biological transportation networks, and focuses on synergies between network science and data analysis, which enable us to determine functional connectivity patterns in multicellular systems.
Journal ArticleDOI
Coupling dynamics of epidemic spreading and information diffusion on complex networks.
Xiu-Xiu Zhan,Xiu-Xiu Zhan,Chuang Liu,Ge Zhou,Zi-Ke Zhang,Zi-Ke Zhang,Gui-Quan Sun,Jonathan J. H. Zhu,Zhen Jin +8 more
TL;DR: This work analyzes the propagation of two representative diseases in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes.
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References
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