Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

What's in a crowd? Analysis of face-to-face behavioral networks

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 important differences as well as striking 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

2 P2P or Not 2 P2P?

In the hope of stimulating discussion, we present a heuristic decision tree that designers can use to judge the likely suitability of a P2P architecture for their applications. It is based on the characteristics of a wide range of P2P systems from the literature, both proposed and deployed.Comment: 6 pages, 1 figur

Beyond Counting: New Perspectives on the Active IPv4 Address Space

In this study, we report on techniques and analyses that enable us to capture Internet-wide activity at individual IP address-level granularity by relying on server logs of a large commercial content delivery network (CDN) that serves close to 3 trillion HTTP requests on a daily basis. Across the whole of 2015, these logs recorded client activity involving 1.2 billion unique IPv4 addresses, the highest ever measured, in agreement with recent estimates. Monthly client IPv4 address counts showed constant growth for years prior, but since 2014, the IPv4 count has stagnated while IPv6 counts have grown. Thus, it seems we have entered an era marked by increased complexity, one in which the sole enumeration of active IPv4 addresses is of little use to characterize recent growth of the Internet as a whole. With this observation in mind, we consider new points of view in the study of global IPv4 address activity. Our analysis shows significant churn in active IPv4 addresses: the set of active IPv4 addresses varies by as much as 25% over the course of a year. Second, by looking across the active addresses in a prefix, we are able to identify and attribute activity patterns to network restructurings, user behaviors, and, in particular, various address assignment practices. Third, by combining spatio-temporal measures of address utilization with measures of traffic volume, and sampling-based estimates of relative host counts, we present novel perspectives on worldwide IPv4 address activity, including empirical observation of under-utilization in some areas, and complete utilization, or exhaustion, in others.Comment: in Proceedings of ACM IMC 201