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Showing papers by "Vito Latora published in 2012"


Journal ArticleDOI
TL;DR: The empirical analysis of a unique data set regarding almost 200 years of evolution of the road network in a large area located north of Milan (Italy) finds that urbanisation is characterised by the homogenisation of cell shapes, and by the stability throughout time of high–centrality roads which constitute the backbone of the urban structure.
Abstract: Urbanisation is a fundamental phenomenon whose quantitative characterisation is still inadequate. We report here the empirical analysis of a unique data set regarding almost 200 years of evolution of the road network in a large area located north of Milan (Italy). We find that urbanisation is characterised by the homogenisation of cell shapes, and by the stability throughout time of high–centrality roads which constitute the backbone of the urban structure, confirming the importance of historical paths. We show quantitatively that the growth of the network is governed by two elementary processes: (i) ‘densification’, corresponding to an increase in the local density of roads around existing urban centres and (ii) ‘exploration’, whereby new roads trigger the spatial evolution of the urbanisation front. The empirical identification of such simple elementary mechanisms suggests the existence of general, simple properties of urbanisation and opens new directions for its modelling and quantitative description.

296 citations


Journal ArticleDOI
TL;DR: In this paper, the authors examined the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain and found that the correlation is higher with secondary than primary activities.
Abstract: The paper examines the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain. The focus is on what type of street centrality (closeness, betweenness and straightness) is more closely associated with which type of economic activity (primary and secondary). Centralities are calculated purely on the street network by using a multiple centrality assessment model, and a kernel density estimation method is applied to both street centralities and economic activities to permit correlation analysis between them. Results indicate that street centralities are correlated with the location of economic activities and that the correlations are higher with secondary than primary activities. The research suggests that, in urban planning, central urban arterials should be conceived as the cores, not the borders, of neighbourhoods.

239 citations


Journal ArticleDOI
TL;DR: In this article, an entire society of individuals, the players of an online-game, with complete information on their movements in a network-shaped universe and on their social and economic interactions is studied.
Abstract: Despite the recent availability of large data sets on human movements, a full understanding of the rules governing motion within social systems is still missing, due to incomplete information on the socio-economic factors and to often limited spatio-temporal resolutions. Here we study an entire society of individuals, the players of an online-game, with complete information on their movements in a network-shaped universe and on their social and economic interactions. Such a "socio-economic laboratory" allows to unveil the intricate interplay of spatial constraints, social and economic factors, and patterns of mobility. We find that the motion of individuals is not only constrained by physical distances, but also strongly shaped by the presence of socio-economic areas. These regions can be recovered perfectly by community detection methods solely based on the measured human dynamics. Moreover, we uncover that long-term memory in the time-order of visited locations is the essential ingredient for modeling the trajectories.

151 citations


Journal ArticleDOI
04 Apr 2012-Chaos
TL;DR: In this article, the problem of finding strongly connected components in a time-varying graph can be mapped into finding the maximal-cliques in an opportunely constructed static graph, which is called the affine graph.
Abstract: Real complex systems are inherently time-varying. Thanks to new communication systems and novel technologies, today it is possible to produce and analyze social and biological networks with detailed information on the time of occurrence and duration of each link. However, standard graph metrics introduced so far in complex network theory are mainly suited for static graphs, i.e., graphs in which the links do not change over time, or graphs built from time-varying systems by aggregating all the links as if they were concurrent in time. In this paper, we extend the notion of connectedness, and the definitions of node and graph components, to the case of time-varying graphs, which are represented as time-ordered sequences of graphs defined over a fixed set of nodes. We show that the problem of finding strongly connected components in a time-varying graph can be mapped into the problem of discovering the maximal-cliques in an opportunely constructed static graph, which we name the affine graph. It is, therefore, an NP-complete problem. As a practical example, we have performed a temporal component analysis of time-varying graphs constructed from three data sets of human interactions. The results show that taking time into account in the definition of graph components allows to capture important features of real systems. In particular, we observe a large variability in the size of node temporal in- and out-components. This is due to intrinsic fluctuations in the activity patterns of individuals, which cannot be detected by static graph analysis.

122 citations


Journal ArticleDOI
TL;DR: It is shown that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links.
Abstract: Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute node centralities once the network structure is assigned. We face here with the inverse problem, that is, we study how to modify the centrality scores of the nodes by acting on the structure of a given network. We show that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links. We found that many large networks from the real world have surprisingly small controlling sets, containing even less than 5 – 10% of the nodes.

73 citations


Journal ArticleDOI
TL;DR: In this article, the structural properties of the street networks of ten different European cities using their primal representation are investigated, and a set of centrality measures highlighting differences and similarities among cases are presented.
Abstract: We compare the structural properties of the street networks of ten different European cities using their primal representation. We investigate the properties of the geometry of the networks and a set of centrality measures highlighting differences and similarities among cases. In particular, we found that cities share structural similarities due to their quasi planarity but that there are also several distinctive geometrical proprieties. A Principal Component Analysis is also performed on the distributions of centralities and their respective moments, which is used to find distinctive characteristics by which we can classify cities into families. We believe that, beyond the improvement of the empirical knowledge on streets network proprieties, our findings can open new perspectives in the scientific relation between city planning and complex networks, stimulating the debate on the effectiveness of the set of knowledge that statistical physics can contribute for city planning and urban morphology studies.

58 citations


Posted Content
TL;DR: In this article, a unique data set regarding almost 200 years of evolution of the road network in a large area located north of Milan (Italy) was used to investigate the role of high-centrality roads in urban growth.
Abstract: Urbanisation is a fundamental phenomenon whose quantitative characterisation is still inadequate. We report here the empirical analysis of a unique data set regarding almost 200 years of evolution of the road network in a large area located north of Milan (Italy). We find that urbanisation is characterised by the homogenisation of cell shapes, and by the stability throughout time of high-centrality roads which constitute the backbone of the urban structure, confirming the importance of historical paths. We show quantitatively that the growth of the network is governed by two elementary processes: (i) `densification', corresponding to an increase in the local density of roads around existing urban centres and (ii) `exploration', whereby new roads trigger the spatial evolution of the urbanisation front. The empirical identification of such simple elementary mechanisms suggests the existence of general, simple properties of urbanisation and opens new directions for its modelling and quantitative description.

48 citations


Journal ArticleDOI
TL;DR: It is pointed out that a strong interplay between network growth and disease spreading produces networks with degree–degree correlations and nontrivial clustering patterns.
Abstract: We study the evolution of an adaptive network whose growth occurs simultaneously to the propagation of a disease. The dynamics of the network growth is entangled to the spread of the disease, since the probability for a node in the network to get new links depends on its healthy or infected state. We analyze the influence that such coupling mechanism has both on the diffusion of the disease and on the structure of the growing networks. Our results point out that a strong interplay between network growth and disease spreading produces networks with degree–degree correlations and nontrivial clustering patterns.

6 citations


Journal ArticleDOI
TL;DR: An analytical law is derived connecting the standard deviation of flows and their mean values and it is proved that the results are robust under different assumptions regarding network topology, routing strategy and packets injection distributions.
Abstract: Communication networks are nowadays crucial in our lives and the study of the traffic features yields important advantages. In both network and traffic design, the understanding of the relationship between the traffic on a node and its fluctuations plays a key role. In this paper, we investigate the relationship between the mean traffic flow experienced by a node and its standard deviation via numerical simulations and real data analysis. In particular, we show the great influence that the degree heterogeneity of real communication systems has on the patterns of flow fluctuations observed across complex communication networks. To this end, we derive an analytical law connecting the standard deviation of flows and their mean values, we prove it via extensive numerical simulations and by means of a realistic internet traffic simulator software: NS-3. We also show that our results are robust under different assumptions regarding network topology, routing strategy and packets injection distributions.

4 citations


Journal ArticleDOI
TL;DR: In this article, Simmelian brokerage is proposed to capture the opportunities of brokerage between otherwise disconnected cohesive groups of contacts, which can be expressed one in terms of the other through a simple functional relation.
Abstract: In the social sciences, the debate over the structural foundations of social capital has long vacillated between two positions on the relative benefits associated with two types of social structures: closed structures, rich in third-party relationships, and open structures, rich in structural holes and brokerage opportunities. In this paper, we engage with this debate by focusing on the measures typically used for formalising the two conceptions of social capital: clustering and effective size. We show that these two measures are simply two sides of the same coin, as they can be expressed one in terms of the other through a simple functional relation. Building on this relation, we then attempt to reconcile closed and open structures by proposing a new measure, Simmelian brokerage, that captures opportunities of brokerage between otherwise disconnected cohesive groups of contacts. Implications of our findings for research on social capital and complex networks are discussed.