Petaluma, CA, United States
Petaluma, CA, United States

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Cattuto C.,Turin Networks | van den Broeck W.,Turin Networks | Barrat A.,Turin Networks | Barrat A.,French National Center for Scientific Research | And 4 more authors.
PLoS ONE | Year: 2010

Background: Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. Methods and Findings: We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Conclusions: Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of superconnector behavior in the community. These results could impact our understanding of all phenomena driven by face-toface interactions, such as the spreading of transmissible infectious diseases and information. © 2010 Cattuto et al.

Baronchelli A.,Polytechnic University of Catalonia | Gong T.,Max Planck Institute for Evolutionary Anthropology | Puglisi A.,National Research Council Italy | Puglisi A.,University of Rome La Sapienza | And 2 more authors.
Proceedings of the National Academy of Sciences of the United States of America | Year: 2010

The empirical evidence that human color categorization exhibits some universal patterns beyond superficial discrepancies across different cultures is a major breakthrough in cognitive science. As observed in the World Color Survey (WCS), indeed, any two groups of individuals develop quite different categorization patterns, but some universal properties can be identified by a statistical analysis over a large number of populations. Here, we reproduce the WCS in a numerical model in which different populations develop independently their own categorization systems by playing elementary language games.We find that a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference (JND), is sufficient to trigger the emergence of universal patterns that unconstrained cultural interaction fails to produce. We test the results of our experiment against real data by performing the same statistical analysis proposed to quantify the universal tendencies shown in the WCS [Kay P & Regier T. (2003) Proc. Natl. Acad. Sci. USA 100: 9085-9089], and obtain an excellent quantitative agreement. This work confirms that synthetic modeling has nowadays reached the maturity to contribute significantly to the ongoing debate in cognitive science.

Isella L.,Turin Networks | Stehle J.,French National Center for Scientific Research | Barrat A.,Turin Networks | Barrat A.,French National Center for Scientific Research | And 3 more authors.
Journal of Theoretical Biology | Year: 2011

The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks. © 2010 Elsevier Ltd.

Zhao K.,Northeastern University | Stehle J.,French National Center for Scientific Research | Bianconi G.,Northeastern University | Barrat A.,French National Center for Scientific Research | Barrat A.,Turin Networks
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2011

The recent availability of data describing social networks is changing our understanding of the "microscopic structure" of a social tie. A social tie indeed is an aggregated outcome of many social interactions such as face-to-face conversations or phone calls. Analysis of data on face-to-face interactions shows that such events, as many other human activities, are bursty, with very heterogeneous durations. In this paper we present a model for social interactions at short time scales, aimed at describing contexts such as conference venues in which individuals interact in small groups. We present a detailed analytical and numerical study of the model's dynamical properties, and show that it reproduces important features of empirical data. The model allows for many generalizations toward an increasingly realistic description of social interactions. In particular, in this paper we investigate the case where the agents have intrinsic heterogeneities in their social behavior, or where dynamic variations of the local number of individuals are included. Finally we propose this model as a very flexible framework to investigate how dynamical processes unfold in social networks. © 2011 American Physical Society.

Stehle J.,French National Center for Scientific Research | Barrat A.,French National Center for Scientific Research | Barrat A.,Turin Networks | Bianconi G.,Northeastern University
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2010

We present a modeling framework for dynamical and bursty contact networks made of agents in social interaction. We consider agents' behavior at short time scales in which the contact network is formed by disconnected cliques of different sizes. At each time a random agent can make a transition from being isolated to being part of a group or vice versa. Different distributions of contact times and intercontact times between individuals are obtained by considering transition probabilities with memory effects, i.e., the transition probabilities for each agent depend both on its state (isolated or interacting) and on the time elapsed since the last change in state. The model lends itself to analytical and numerical investigations. The modeling framework can be easily extended and paves the way for systematic investigations of dynamical processes occurring on rapidly evolving dynamical networks, such as the propagation of an information or spreading of diseases. © 2010 The American Physical Society.

Lancichinetti A.,Turin Networks | Radicchi F.,Polytechnic University of Turin | Ramasco J.J.,Polytechnic University of Turin
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2010

Nodes in real-world networks are usually organized in local modules. These groups, called communities, are intuitively defined as subgraphs with a larger density of internal connections than of external links. In this work, we define a measure aimed at quantifying the statistical significance of single communities. Extreme and order statistics are used to predict the statistics associated with individual clusters in random graphs. These distributions allows us to define one community significance as the probability that a generic clustering algorithm finds such a group in a random graph. The method is successfully applied in the case of real-world networks for the evaluation of the significance of their communities. © 2010 The American Physical Society.

Radicchi F.,Turin Networks | Fortunato S.,Turin Networks
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2010

Percolation is one of the most studied processes in statistical physics. A recent paper by Achlioptas [Science 323, 1453 (2009)] showed that the percolation transition, which is usually continuous, becomes discontinuous ("explosive") if links are added to the system according to special cooperative rules (Achlioptas processes). In this paper, we present a detailed numerical analysis of Achlioptas processes with product rule on various systems, including lattices, random networks á la Erdös-Rényi, and scale-free networks. In all cases, we recover the explosive transition by Achlioptas However, the explosive percolation transition is kind of hybrid as, despite the discontinuity of the order parameter at the threshold, one observes traces of analytical behavior such as power-law distributions of cluster sizes. In particular, for scale-free networks with degree exponent λ<3, all relevant percolation variables display power-law scaling, just as in continuous second-order phase transitions. © 2010 The American Physical Society.

Fortunato S.,Turin Networks | Radicchi F.,Northwestern University
Journal of Physics: Conference Series | Year: 2011

Percolation is perhaps the simplest example of a process exhibiting a phase transition and one of the most studied phenomena in statistical physics. The percolation transition is continuous if sites/bonds are occupied independently with the same probability. However, alternative rules for the occupation of sites/bonds might affect the order of the transition. A recent set of rules proposed by Achlioptas et al. [Science 323, 1453 (2009)], characterized by competitive link addition, was claimed to lead to a discontinuous connectedness transition, named "explosive percolation". In this work we survey a numerical study of the explosive percolation transition on various types of graphs, from lattices to scale-free networks, and show the consistency of these results with recent analytical work showing that the transition is actually continuous.

News Article | January 8, 2009

Force 10 Networks is to merge with Turin Networks, a provider of wireless backhaul, Carrier Ethernet and converged access systems for service providers. The agreement is another example of consolidation in the Ethernet switching marketplace as Cisco maintains its dominance and Juniper ramps up its presence in the market. Last year saw Foundry Networks merge with Brocade, and Enterasys Networks link up with Siemens Enterprise Communications. The combined companies will have more than 1,300 customers worldwide and a product portfolio they say will serve both the enterprise and service provider markets through existing sales channels. Both companies are privately-held, so financial details of the deal were not disclosed, although the merged company will carry the Force 10 Networks name. Henry Wasik, president and CEO of Turin Networks, will become the president and CEO of the combined entity. Current Force10 Networks President and CEO James Hanley will assume the role of president, field operations, with responsibility for sales, marketing, services and business development. According to Force 10, the merger creates operational efficiencies and market focus in two high-growth networking segments. Over time, the new company will deliver products through the integration of Turin's wireless backhaul, metro service edge and converged access platforms with Force10's access switches. Turin's wireless backhaul products are deployed in more than 60,000 North American cell sites. Force 10 has less than a 1 percent share of the overall $18 billion (£11.9 billion) Ethernet switch market, but a much more significant share of the total 10 Gigabit Ethernet market, according to Dell'Oro Group. Citing data from market research firms IDC and Ovum RHK, the companies say 2 million 10 Gigabit Ethernet ports will be deployed in data centres worldwide next year, while data centre Ethernet switching revenue will grow at a compound annual rate of 8 percent to reach $6.4 billion in 2012. Meanwhile, the market for pseudowire and Ethernet backhaul transport equipment is expected to grow by more than a factor of 10 between 2008 and 2012, reaching more than $5 billion in 2012, the companies say. The merger is expected to be finalised by March 2009, pending the completion of legal and regulatory filings. The combined company will be headquartered in San Jose, California.

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