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Showing papers by "Vito Latora published in 2011"


Book ChapterDOI
12 Jun 2011
TL;DR: NextPlace is presented, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places that achieves higher performance compared to other predictors and also more stability over time.
Abstract: Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future. In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.

320 citations


Journal ArticleDOI
TL;DR: The notion of connectedness, and the definitions of node and graph components, are extended to the case of time-varying graphs, which are represented as time-ordered sequences of graphs defined over a fixed set of nodes.
Abstract: Real complex systems are inherently time-varying. Thanks to new communication systems and novel technologies, it is today 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.

123 citations


Journal ArticleDOI
TL;DR: It is demonstrated that an almost maximal-entropy random walk is obtained when the step probabilities are proportional to a power of the degree of the target node, with an exponent α that depends on the degree-degree correlations and is equal to 1 in uncorrelated graphs.
Abstract: Maximization of the entropy rate is an important issue to design diffusion processes aiming at a well-mixed state. We demonstrate that it is possible to construct maximal-entropy random walks with only local information on the graph structure. In particular, we show that an almost maximal-entropy random walk is obtained when the step probabilities are proportional to a power of the degree of the target node, with an exponent α that depends on the degree-degree correlations and is equal to 1 in uncorrelated graphs.

123 citations


Journal ArticleDOI
TL;DR: The competition between these two adaptive principles leads to the emergence of key structural properties observed in real world networks, such as modular and scale–free structures, together with a striking enhancement of local synchronization in systems with no global order.
Abstract: Synchronization is a collective phenomenon occurring in systems of interacting units, and is ubiquitous in nature, society and technology. Recent studies have enlightened the important role played by the interaction topology on the emergence of synchronized states. However, most of these studies neglect that real world systems change their interaction patterns in time. Here, we analyze synchronization features in networks in which structural and dynamical features co-evolve. The feedback of the node dynamics on the interaction pattern is ruled by the competition of two mechanisms: homophily (reinforcing those interactions with other correlated units in the graph) and homeostasis (preserving the value of the input strength received by each unit). The competition between these two adaptive principles leads to the emergence of key structural properties observed in real world networks, such as modular and scale–free structures, together with a striking enhancement of local synchronization in systems with no global order.

102 citations


Journal ArticleDOI
TL;DR: This work introduces the concept of flow graphs, namely weighted networks where dynamical flows are embedded into the link weights, and focuses on the mathematical properties of generic linear processes on complex networks that can be represented as biased random walks and their dual consensus dynamics.
Abstract: The behavior of complex systems is determined not only by the topological organization of their interconnections but also by the dynamical processes taking place among their constituents. A faithful modeling of the dynamics is essential because different dynamical processes may be affected very differently by network topology. A full characterization of such systems thus requires a formalization that encompasses both aspects simultaneously, rather than relying only on the topological adjacency matrix. To achieve this, we introduce the concept of flow graphs, namely weighted networks where dynamical flows are embedded into the link weights. Flow graphs provide an integrated representation of the structure and dynamics of the system, which can then be analyzed with standard tools from network theory. Conversely, a structural network feature of our choice can also be used as the basis for the construction of a flow graph that will then encompass a dynamics biased by such a feature. We illustrate the ideas by focusing on the mathematical properties of generic linear processes on complex networks that can be represented as biased random walks and their dual consensus dynamics, and show how our framework improves our understanding of these processes.

89 citations


Journal ArticleDOI
TL;DR: It is shown that a competitive mechanism leads to the emergence of a rich modular structure underlying cluster synchronization, and to a scale-free distribution for the connection strengths of the units.
Abstract: We consider a set of interacting phase oscillators, with a coupling between synchronized nodes adaptively reinforced, and the constraint of a limited resource for a node to establish connections with the other units of the network. We show that such a competitive mechanism leads to the emergence of a rich modular structure underlying cluster synchronization, and to a scale-free distribution for the connection strengths of the units.

80 citations


Proceedings ArticleDOI
20 Jun 2011
TL;DR: A time-aware containment strategy that spreads a patch message starting from nodes with high temporal closeness centrality and it is shown that this scheme reduces the cellular network resource consumption and associated costs, achieving complete containment of malware in a limited amount of time.
Abstract: Malicious mobile phone worms spread between devices via short-range Bluetooth contacts, similar to the propagation of human and other biological viruses. Recent work has employed models from epidemiology and complex networks to analyse the spread of malware and the effect of patching specific nodes. These approaches have adopted a static view of the mobile networks, i.e., by aggregating all the edges that appear over time, which leads to an approximate representation of the real interactions: instead, these networks are inherently dynamic and the edge appearance and disappearance are highly influenced by the ordering of the human contacts, something which is not captured at all by existing complex network measures. In this paper we first study how the blocking of malware propagation through immunisation of key nodes (even if carefully chosen through static or temporal betweenness centrality metrics) is ineffective: this is due to the richness of alternative paths in these networks. Then we introduce a time-aware containment strategy that spreads a patch message starting from nodes with high temporal closeness centrality and show its effectiveness using three real-world datasets. Temporal closeness allows the identification of nodes able to reach most nodes quickly: we show that this scheme reduces the cellular network resource consumption and associated costs, achieving, at the same time, complete containment of malware in a limited amount of time.

42 citations


Journal ArticleDOI
01 Jun 2011-EPL
TL;DR: In this article, the authors consider a model in which agents of different species move over a complex network, are subject to reproduction and compete for resources, and the complementary roles of competition and diffusion produce a variety of fixed points, whose stability depends on the structure of the underlying complex network.
Abstract: We consider a model in which agents of different species move over a complex network, are subject to reproduction and compete for resources. The complementary roles of competition and diffusion produce a variety of fixed points, whose stability depends on the structure of the underlying complex network. The survival and death of species is influenced by the network degree distribution, clustering, degree-degree correlations and community structures. We found that the invasion of all the nodes by just one species is possible only in Erdos-Renyi and regular graphs, while networks with scale-free degree distribution, as those observed in real social, biological and technological systems, guarantee the coexistence of different species and therefore help enhancing species diversity.

23 citations


Journal ArticleDOI
TL;DR: It is found that the motion of individuals is not only constrained by physical distances, but also strongly shaped by the presence of socio-economic areas, which means that long-term memory in the time-order of visited locations is the essential ingredient for modeling the trajectories.
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.

14 citations


01 Jan 2011
TL;DR: In this paper, an analysis of the italian highvoltage power grid based on a mapping between power grid nodes and Kuramoto-like oscillators is proposed, which is able to reach a global synchronous state, after which a perturbation is applied in order to study the dynamical robustness of the network to faults.
Abstract: In this work, an analysis of the italian high-voltage power grid based on a mapping between power grid nodes and Kuramoto-like oscillators is proposed. The network is able to reach a global synchronous state, after which a perturbation is applied in order to study the dynamical robustness of the network to faults. Several dynamical parameters such as the minimum value of perturbation leading to desynchronization and the time to reach the complete loss of synchronism have been introduced. A non-trivial complex relationship between dynamical and topological parameters of the network emerges.

8 citations


Journal ArticleDOI
01 Jun 2011-EPL
TL;DR: In this paper, the authors investigate flow dynamics in rivers characterized by basin areas and daily mean discharge spanning different orders of magnitude and show that the delayed increments evaluated at time scales ranging from days to months can be rescaled to the same non-Gaussian probability density function.
Abstract: We investigate flow dynamics in rivers characterized by basin areas and daily mean discharge spanning different orders of magnitude. We show that the delayed increments evaluated at time scales ranging from days to months can be opportunely rescaled to the same non-Gaussian probability density function. Such a scaling breaks up above a certain critical horizon, where a behavior typical of thermodynamic systems at the critical point emerges. We finally show that both the scaling behavior and the break-up of the scaling are universal features of river flow dynamics. Copyright c EPLA, 2011

Posted Content
TL;DR: In this article, it was shown that the spectral centrality of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute the node centralities once the network structure is assigned.
Abstract: Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by their 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 the 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 prove 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 show that many large networks from the real world have surprisingly small controlling sets, containing even less than 5-10% of the nodes. These results suggest that rankings obtained from spectral centrality measures have to be considered with extreme care, since they can be easily controlled and even manipulated by a small group of nodes acting in a coordinated way.