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We analyze information diffusion using empirical data that tracks online communication around two instances of mass political mobilization that took place in Spain in 2011 and 2012. We also analyze protest-related communications during the year that elapsed between those protests. We compare the global properties of the topological and dynamic networks through which communication took place, as well as local changes in network composition. We show that changes in network structure underlie aggregated differences on how information diffused: an increase in network hierarchy is accompanied by a reduction in the average size of cascades. The increasing hierarchy affects not only the underlying communication topology but also the more dynamic structure of information exchange ; the increase is especially noticeable amongst certain categories of nodes (or users). Our findings suggest that the relationship between the structure of networks and their function in diffusing information is not as straightforward as some theoretical models of diffusion in networks imply.

This article explores the growth of online mobilizations using data from the indignados (outraged) movement in Spain, which emerged under the influence of the revolution in Egypt and as a precursor to the global Occupy mobilizations. The data track Twitter activity around the protests that took place in May 2011, which led to the formation of camp sites in dozens of cities all over the country and massive daily demonstrations during the week prior to the elections of May 22. We reconstruct the network of tens of thousands of users and monitor their message activity for a month (April 25, 2011, to May 25, 2011). Using both the structure of the network and levels of activity in message exchange, we identify four types of users and analyze their role in the growth of the protest. Drawing from theories of online activism and research on information diffusion in networks, this article centers on the following two questions: How does protest information spread in online networks? And how do different actors contribute to the growth of activity? The article aims to inform the theoretical debate on whether digital technologies are changing the logic of collective action and to provide evidence of how new media facilitates the emergence of massive offline mobilizations.

Abstract
The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that (1) people conserve a small number of repeatedly used routes and (2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.

Abstract
The construction of ancient road networks spanned generations and exhibits temporal path dependence that is not fully captured by established network formation models that are used to support archaeological reasoning. We introduce an evolutionary model that captures explicitly the sequential nature of road network formation: A central feature is that connections are added successively and according to an optimal cost–benefit trade-off with respect to existing connections. In this model, the network topology emerges rapidly from early decisions, a trait that makes it possible to identify plausible road construction orders in practice. Based on this observation we develop a method to compress the search space of path-dependent optimization problems. We use this method to show that the model's assumptions on ancient decision-making allow the reconstruction of partially known road networks from the Roman era in good detail and from sparse archaeological evidence. In particular, we identify missing links in the major road network of ancient Sardinia that are in good agreement with expert predictions.

Date of Acceptance: 06/11/2014 We are thankful to Prof Alex Arenas, Dr Sergio Gómez, Veronika Stolbova and Dominik Traxl for their helpful comments. We also thank Joaquín Sanz Remón for kindly providing the data of the Tuberculosis RT network and for his valuable comments. This work has been supported by (JK) the German Federal Ministry of Education and Research (Bernstein Center II, grant no. 01GQ1001A), (FK) the Engineering and Physical Sciences Research Council, and (GZL) the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement number PIEF- GA-2012-331800. ; Peer reviewed ; Publisher PDF

Abstract
Routing choices of walking pedestrians in geometrically complex environments are regulated by the interplay of a multitude of factors such as local crowding, (estimated) time to destination, and (perceived) comfort. As individual choices combine, macroscopic traffic flow patterns emerge. Understanding the physical mechanisms yielding macroscopic traffic distributions in environments with complex geometries is an outstanding scientific challenge, with implications in the design and management of crowded pedestrian facilities. In this work, we analyze, by means of extensive real-life pedestrian tracking data, unidirectional flow dynamics in an asymmetric setting, as a prototype for many common complex geometries. Our environment is composed of a main walkway and a slightly longer detour. Our measurements have been collected during a dedicated high-accuracy pedestrian tracking campaign held in Eindhoven (The Netherlands). We show that the dynamics can be quantitatively modeled by introducing a collective discomfort function, and that fluctuations on the behavior of single individuals are crucial to correctly recover the global statistical behavior. Notably, the observed traffic split substantially departs from an optimal, transport-wise, partition, as the global pedestrian throughput is not maximized.

Abstract
Global patterns of collective motion in bird flocks, fish schools, and human crowds are thought to emerge from local interactions within a neighborhood of interaction, the zone in which an individual is influenced by their neighbors. Both metric and topological neighborhoods have been reported in animal groups, but this question has not been addressed for human crowds. The answer has important implications for modeling crowd behavior and predicting crowd disasters such as jams, crushes, and stampedes. In a metric neighborhood, an individual is influenced by all neighbors within a fixed radius, whereas in a topological neighborhood, an individual is influenced by a fixed number of nearest neighbors, regardless of their physical distance. A recently proposed alternative is a visual neighborhood, in which an individual is influenced by the optical motions of all visible neighbors. We test these hypotheses experimentally by asking participants to walk in real and virtual crowds and manipulating the crowd's density. Our results rule out a topological neighborhood, are approximated by a metric neighborhood, but are best explained by a visual neighborhood that has elements of both. We conclude that the neighborhood of interaction in human crowds follows naturally from the laws of optics and suggest that previously observed "topological" and "metric" interactions might be a consequence of the visual neighborhood.

Understanding human interactions in online communications is of paramount importance for our society. Alarming phenomena such as the spreading of fake news or the formation of echo-chambers can emerge in unhealthy communication environments and, ultimately, undermine the democratic discourse. In this context, unveiling the individual drivers that give rise to collective attention can help to conserve the health of our information ecosystems. Here, following a recently proposed analogy between natural and information ecosystems, we explore how competition for attention in online social networks and the strategies adopted by the users to maximize their visibility shape our communication dynamics. Specifically, by analyzing large-scale datasets from the micro-blogging platform Twitter and performing numerical modeling of the system dynamics, we are able to measure the amount of competition for attention experienced by users and how it changes when exogenous events captivate collective attention. The work relies on topic modeling to extract users' interests and memes context from the data and a framework based on ecological niche theory to quantify the strength of negative (competitive) and positive (mutualistic) interactions for both users and memes. Interestingly, our findings show two different behaviors. While memes undergo a sharp increase in competition during exceptional events that can lead to their extinction, users perceive a decrease in effective competition due to a stronger effect of mutualistic interaction, explaining the focus of collective attention around specific topics. Finally, to confirm our results we reproduce the observed shifts with a data-driven model of species dynamics. ; MJP, AS-R and JB-H acknowledge the support of the Spanish MICINN Project PGC2018-096999-A-I00. EP acknowledges a fellowship funded by the Stazione Zoologica Anton Dohrn (SZN) within the SZN-Open University PhD program. SS thanks the support of UNIPD through ReACT Stars 2018 grant and INFN LINCOLN grant. SM and VC-S acknowledge funding from the project PACSS RTI2018-093732-B-C22 of the MCIN/AEI /10.13039/501100011033/ and by EU through FEDER funds (Away to make Europe), and also from the Maria de Maeztu program MDM-2017-0711 of the MCIN/AEI /10.13039/501100011033. All authors acknowledge the support to the TEAMS project of the Cariparo Visiting Program 2018 (Padova, Italy).

At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we cannot disregard the most vulnerable elements in the urban landscape: pedestrians, exposed to higher risks than other road users. Indeed, safe, accessible, and sustainable transport systems in cities are a core target of the UN's 2030 Agenda. Thus, there is an opportunity to apply advanced computational tools to the problem of traffic safety, in regards especially to pedestrians, who have been often overlooked in the past. This paper combines public data sources, large-scale street imagery and computer vision techniques to approach pedestrian and vehicle safety with an automated, relatively simple, and universally-applicable data-processing scheme. The steps involved in this pipeline include the adaptation and training of a Residual Convolutional Neural Network to determine a hazard index for each given urban scene, as well as an interpretability analysis based on image segmentation and class activation mapping on those same images. Combined, the outcome of this computational approach is a fine-grained map of hazard levels across a city, and an heuristic to identify interventions that might simultaneously improve pedestrian and vehicle safety. The proposed framework should be taken as a complement to the work of urban planners and public authorities.