168 research outputs found
Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models
Epidemics-inspired techniques have received huge attention in recent years
from the distributed systems and networking communities. These algorithms and
protocols rely on probabilistic message replication and redundancy to ensure
reliable communication. Moreover, they have been successfully exploited to
support group communication in distributed systems, broadcasting, multicasting
and information dissemination in fixed and mobile networks. However, in most of
the existing work, the probability of infection is determined heuristically,
without relying on any analytical model. This often leads to unnecessarily high
transmission overheads.
In this paper we show that models of epidemic spreading in complex networks
can be applied to the problem of tuning and controlling the dissemination of
information in wireless ad hoc networks composed of devices carried by
individuals, i.e., human-based networks. The novelty of our idea resides in the
evaluation and exploitation of the structure of the underlying human network
for the automatic tuning of the dissemination process in order to improve the
protocol performance. We evaluate the results using synthetic mobility models
and real human contacts traces
Correlating densities of centrality and activities in cities : the cases of Bologna (IT) and Barcelona (ES)
This paper examines the relationship between street centrality and densities of commercial and service activities in cities. The aim is to verify whether a correlation exists and whether some 'secondary' activities, i.e. those scarcely specialized oriented to the general public and ordinary daily life, are more linked to street centrality than others. The metropolitan area of Barcelona (Spain) is investigated, and results are compared with those found in a previous work on the city of Bologna (Italy). Street centrality is calibrated in a multiple centrality assessment (MCA) model composed of multiple measures such as closeness, betweenness and straightness. Kernel density estimation (KDE) is used to transform data sets of centrality and activities to one scale unit for correlation analysis between them. Results indicate that retail and service activities in both Bologna and Barcelona tend to concentrate in areas with better centralities, and that secondary activities exhibit a higher correlation
Small-world behavior in time-varying graphs
Connections in complex networks are inherently fluctuating over time and
exhibit more dimensionality than analysis based on standard static graph
measures can capture. Here, we introduce the concepts of temporal paths and
distance in time-varying graphs. We define as temporal small world a
time-varying graph in which the links are highly clustered in time, yet the
nodes are at small average temporal distances. We explore the small-world
behavior in synthetic time-varying networks of mobile agents, and in real
social and biological time-varying systems.Comment: 5 pages, 2 figure
Structural Properties of Planar Graphs of Urban Street Patterns
Recent theoretical and empirical studies have focused on the structural
properties of complex relational networks in social, biological and
technological systems. Here we study the basic properties of twenty
1-square-mile samples of street patterns of different world cities. Samples are
represented by spatial (planar) graphs, i.e. valued graphs defined by metric
rather than topologic distance and where street intersections are turned into
nodes and streets into edges. We study the distribution of nodes in the
2-dimensional plane. We then evaluate the local properties of the graphs by
measuring the meshedness coefficient and counting short cycles (of three, four
and five edges), and the global properties by measuring global efficiency and
cost. As normalization graphs, we consider both minimal spanning trees (MST)
and greedy triangulations (GT) induced by the same spatial distribution of
nodes. The results indicate that most of the cities have evolved into networks
as efficienct as GT, although their cost is closer to the one of a tree. An
analysis based on relative efficiency and cost is able to characterize
different classes of cities.Comment: 7 pages, 3 figures, 3 table
The Impact of Geographic Distance on Online Social Interactions
Online social networking services entice millions of users to spend hours every day interacting with each other. The focus of this work is to explain the effect that geographic distance has on online social interactions and, simultaneously, to understand the interplay between the social characteristics of friendship ties and their spatial properties. We analyze data from a large-scale online social network, Tuenti, with about 10 million active users: our sample includes user profiles, user home locations and online social interactions among Tuenti members. Our findings support the idea that spatial distance constraints whom users interact with, but not the intensity of their social interactions. Furthermore, friendship ties belonging to denser connected groups tend to arise at shorter spatial distances than social ties established between members belonging to different groups. Finally, we show that our findings mostly do not depend on the age of the users, although younger users seem to be slightly more constrained to shorter geographic distances. Augmenting social structure with geographic information adds a new dimension to social network analysis and a large number of theoretical investigations and practical applications can be pursued for online social systems, with many promising outcomes. As the amount of available location-based data is increasing, our findings and results open the door to future possibilities: researchers would benefit from these insights when studying online social services, while developers should be aware of these additional possibilities when building systems and applications related to online social platforms
A Tale of Many Cities: Universal Patterns in Human Urban Mobility
The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users. Surprisingly, while there are variations in human movement in different cities, our analysis shows that those are predominantly due to different distributions of places across different urban environments. Moreover, a universal law for human mobility is identified, which isolates as a key component the rank-distance, factoring in the number of places between origin and destination, rather than pure physical distance, as considered in some previous works. Building on our findings, we also show how a rank-based movement model accurately captures real human movements in different cities
Markov Chain Methods For Analyzing Complex Transport Networks
We have developed a steady state theory of complex transport networks used to
model the flow of commodity, information, viruses, opinions, or traffic. Our
approach is based on the use of the Markov chains defined on the graph
representations of transport networks allowing for the effective network
design, network performance evaluation, embedding, partitioning, and network
fault tolerance analysis. Random walks embed graphs into Euclidean space in
which distances and angles acquire a clear statistical interpretation. Being
defined on the dual graph representations of transport networks random walks
describe the equilibrium configurations of not random commodity flows on
primary graphs. This theory unifies many network concepts into one framework
and can also be elegantly extended to describe networks represented by directed
graphs and multiple interacting networks.Comment: 26 pages, 4 figure
A minimal model for congestion phenomena on complex networks
We study a minimal model of traffic flows in complex networks, simple enough
to get analytical results, but with a very rich phenomenology, presenting
continuous, discontinuous as well as hybrid phase transitions between a
free-flow phase and a congested phase, critical points and different scaling
behaviors in the system size. It consists of random walkers on a queueing
network with one-range repulsion, where particles can be destroyed only if they
can move. We focus on the dependence on the topology as well as on the level of
traffic control. We are able to obtain transition curves and phase diagrams at
analytical level for the ensemble of uncorrelated networks and numerically for
single instances. We find that traffic control improves global performance,
enlarging the free-flow region in parameter space only in heterogeneous
networks. Traffic control introduces non-linear effects and, beyond a critical
strength, may trigger the appearance of a congested phase in a discontinuous
manner. The model also reproduces the cross-over in the scaling of traffic
fluctuations empirically observed in the Internet, and moreover, a conserved
version can reproduce qualitatively some stylized facts of traffic in
transportation networks
Theoretical approach and impact of correlations on the critical packet generation rate in traffic dynamics on complex networks
Using the formalism of the biased random walk in random uncorrelated networks
with arbitrary degree distributions, we develop theoretical approach to the
critical packet generation rate in traffic based on routing strategy with local
information. We explain microscopic origins of the transition from the flow to
the jammed phase and discuss how the node neighbourhood topology affects the
transport capacity in uncorrelated and correlated networks.Comment: 6 pages, 5 figure
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