14,184 research outputs found
The Influence of Network Topology on Sound Propagation in Granular Materials
Granular materials, whose features range from the particle scale to the
force-chain scale to the bulk scale, are usually modeled as either particulate
or continuum materials. In contrast with either of these approaches, network
representations are natural for the simultaneous examination of microscopic,
mesoscopic, and macroscopic features. In this paper, we treat granular
materials as spatially-embedded networks in which the nodes (particles) are
connected by weighted edges obtained from contact forces. We test a variety of
network measures for their utility in helping to describe sound propagation in
granular networks and find that network diagnostics can be used to probe
particle-, curve-, domain-, and system-scale structures in granular media. In
particular, diagnostics of meso-scale network structure are reproducible across
experiments, are correlated with sound propagation in this medium, and can be
used to identify potentially interesting size scales. We also demonstrate that
the sensitivity of network diagnostics depends on the phase of sound
propagation. In the injection phase, the signal propagates systemically, as
indicated by correlations with the network diagnostic of global efficiency. In
the scattering phase, however, the signal is better predicted by meso-scale
community structure, suggesting that the acoustic signal scatters over local
geographic neighborhoods. Collectively, our results demonstrate how the force
network of a granular system is imprinted on transmitted waves.Comment: 19 pages, 9 figures, and 3 table
Efficiency of informational transfer in regular and complex networks
We analyze the process of informational exchange through complex networks by
measuring network efficiencies. Aiming to study non-clustered systems, we
propose a modification of this measure on the local level. We apply this method
to an extension of the class of small-worlds that includes {\it declustered}
networks, and show that they are locally quite efficient, although their
clustering coefficient is practically zero. Unweighted systems with small-world
and scale-free topologies are shown to be both globally and locally efficient.
Our method is also applied to characterize weighted networks. In particular we
examine the properties of underground transportation systems of Madrid and
Barcelona and reinterpret the results obtained for the Boston subway network.Comment: 10 pages and 9 figure
The power of indirect social ties
While direct social ties have been intensely studied in the context of
computer-mediated social networks, indirect ties (e.g., friends of friends)
have seen little attention. Yet in real life, we often rely on friends of our
friends for recommendations (of good doctors, good schools, or good
babysitters), for introduction to a new job opportunity, and for many other
occasional needs. In this work we attempt to 1) quantify the strength of
indirect social ties, 2) validate it, and 3) empirically demonstrate its
usefulness for distributed applications on two examples. We quantify social
strength of indirect ties using a(ny) measure of the strength of the direct
ties that connect two people and the intuition provided by the sociology
literature. We validate the proposed metric experimentally by comparing
correlations with other direct social tie evaluators. We show via data-driven
experiments that the proposed metric for social strength can be used
successfully for social applications. Specifically, we show that it alleviates
known problems in friend-to-friend storage systems by addressing two previously
documented shortcomings: reduced set of storage candidates and data
availability correlations. We also show that it can be used for predicting the
effects of a social diffusion with an accuracy of up to 93.5%.Comment: Technical Repor
Predictability of conversation partners
Recent developments in sensing technologies have enabled us to examine the
nature of human social behavior in greater detail. By applying an information
theoretic method to the spatiotemporal data of cell-phone locations, [C. Song
et al. Science 327, 1018 (2010)] found that human mobility patterns are
remarkably predictable. Inspired by their work, we address a similar
predictability question in a different kind of human social activity:
conversation events. The predictability in the sequence of one's conversation
partners is defined as the degree to which one's next conversation partner can
be predicted given the current partner. We quantify this predictability by
using the mutual information. We examine the predictability of conversation
events for each individual using the longitudinal data of face-to-face
interactions collected from two company offices in Japan. Each subject wears a
name tag equipped with an infrared sensor node, and conversation events are
marked when signals are exchanged between sensor nodes in close proximity. We
find that the conversation events are predictable to some extent; knowing the
current partner decreases the uncertainty about the next partner by 28.4% on
average. Much of the predictability is explained by long-tailed distributions
of interevent intervals. However, a predictability also exists in the data,
apart from the contribution of their long-tailed nature. In addition, an
individual's predictability is correlated with the position in the static
social network derived from the data. Individuals confined in a community - in
the sense of an abundance of surrounding triangles - tend to have low
predictability, and those bridging different communities tend to have high
predictability.Comment: 38 pages, 19 figure
The architecture of complex weighted networks
Networked structures arise in a wide array of different contexts such as
technological and transportation infrastructures, social phenomena, and
biological systems. These highly interconnected systems have recently been the
focus of a great deal of attention that has uncovered and characterized their
topological complexity. Along with a complex topological structure, real
networks display a large heterogeneity in the capacity and intensity of the
connections. These features, however, have mainly not been considered in past
studies where links are usually represented as binary states, i.e. either
present or absent. Here, we study the scientific collaboration network and the
world-wide air-transportation network, which are representative examples of
social and large infrastructure systems, respectively. In both cases it is
possible to assign to each edge of the graph a weight proportional to the
intensity or capacity of the connections among the various elements of the
network. We define new appropriate metrics combining weighted and topological
observables that enable us to characterize the complex statistical properties
and heterogeneity of the actual strength of edges and vertices. This
information allows us to investigate for the first time the correlations among
weighted quantities and the underlying topological structure of the network.
These results provide a better description of the hierarchies and
organizational principles at the basis of the architecture of weighted
networks
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