text: 181,128,1243238142397812736,572479189,Manlio De Domenico,"Break from #COVID19 to share this great work led by @GiuliaTtt in coll. w/ @ricgallotti We propose a physically grounded way to calculate efficiency of network flows, and show its relevance for functional percolation as opposed to structural percolation. <LINK> <LINK> In parallel, our model w/ @egaltmann for bursts in collective attention just published! <LINK> We make publicly available the multiplex networks: 26M+ social links among 10M+ users during 9 exceptional events. Enjoy! Github: <LINK> <LINK>",https://arxiv.org/abs/2003.11374,"Network science enables the effective analysis of real interconnected systems, characterized by a complex interplay between topology and interconnections strength. It is well-known that the topology of a network affects its resilience to failures or attacks, as well as its functions. Exchanging information is crucial for many real systems: the internet, transportation networks and the brain are key examples. Despite the introduction of measures of efficiency to analyze network flows, i.e. topologies characterized by weighted connectivity, here we show that they fail to capture combined information of link existence and link weight. In this letter we propose a physically-grounded estimator of flow efficiency which can be computed for every weighted network, regardless from the scale and nature of weights and from any (missing) metadata. Remarkably, results show that our estimator captures the heterogeneity of flows along with topological differences and its complement information obtained from percolation analysis of several empirical systems, including transportation, trade, migrations, and brain networks. We show that cutting the heaviest connections may increase the average communication efficiency of the system and hence, counterintuively, a sparser network is not necessarily less efficient. Remarkably, our estimator enables the comparison of communication efficiency of networks arising from different fields, without the possible pitfalls deriving from the scale of flow. ",Quantifying efficient information exchange in real network flows,2,"['Break from #COVID19 to share this great work led by @GiuliaTtt in coll. w/ @ricgallotti \nWe propose a physically grounded way to calculate efficiency of network flows, and show its relevance for functional percolation as opposed to structural percolation.\n\n<LINK> <LINK>', 'In parallel, our model w/ @egaltmann for bursts in collective attention just published! https://t.co/FE399RzTwm\n\nWe make publicly available the multiplex networks: 26M+ social links among 10M+ users during 9 exceptional events. Enjoy!\nGithub: https://t.co/fZBoZ1Ky4g https://t.co/Qm77r63w2G']",20,03,507