8,068 research outputs found
Encoding dynamics for multiscale community detection: Markov time sweeping for the Map equation
The detection of community structure in networks is intimately related to
finding a concise description of the network in terms of its modules. This
notion has been recently exploited by the Map equation formalism (M. Rosvall
and C.T. Bergstrom, PNAS, 105(4), pp.1118--1123, 2008) through an
information-theoretic description of the process of coding inter- and
intra-community transitions of a random walker in the network at stationarity.
However, a thorough study of the relationship between the full Markov dynamics
and the coding mechanism is still lacking. We show here that the original Map
coding scheme, which is both block-averaged and one-step, neglects the internal
structure of the communities and introduces an upper scale, the `field-of-view'
limit, in the communities it can detect. As a consequence, Map is well tuned to
detect clique-like communities but can lead to undesirable overpartitioning
when communities are far from clique-like. We show that a signature of this
behavior is a large compression gap: the Map description length is far from its
ideal limit. To address this issue, we propose a simple dynamic approach that
introduces time explicitly into the Map coding through the analysis of the
weighted adjacency matrix of the time-dependent multistep transition matrix of
the Markov process. The resulting Markov time sweeping induces a dynamical
zooming across scales that can reveal (potentially multiscale) community
structure above the field-of-view limit, with the relevant partitions indicated
by a small compression gap.Comment: 10 pages, 6 figure
On generalized processor sharing and objective functions: analytical framework
Today, telecommunication networks host a wide range of heterogeneous services. Some demand strict delay minima, while others only need a best-effort kind of service. To achieve service differentiation, network traffic is partitioned in several classes which is then transmitted according to a flexible and fair scheduling mechanism. Telecommunication networks can, for instance, use an implementation of Generalized Processor Sharing (GPS) in its internal nodes to supply an adequate Quality of Service to each class. GPS is flexible and fair, but also notoriously hard to study analytically. As a result, one has to resort to simulation or approximation techniques to optimize GPS for some given objective function. In this paper, we set up an analytical framework for two-class discrete-time probabilistic GPS which allows to optimize the scheduling for a generic objective function in terms of the mean unfinished work of both classes without the need for exact results or estimations/approximations for these performance characteristics. This framework is based on results of strict priority scheduling, which can be regarded as a special case of GPS, and some specific unfinished-work properties in two-class GPS. We also apply our framework on a popular type of objective functions, i.e., convex combinations of functions of the mean unfinished work. Lastly, we incorporate the framework in an algorithm to yield a faster and less computation-intensive result for the optimum of an objective function
Isotopic evidence for biogenic molecular hydrogen production in the Atlantic Ocean
Oceans are a net source of molecular hydrogen (H2) to the atmosphere. The production of marine H2 is assumed to be mainly biological by N2 fixation, but photochemical pathways are also discussed. We present measurements of mole fraction and isotopic composition of dissolved and atmospheric H2 from the southern and northern Atlantic between 2008 and 2010. In total almost 400 samples were taken during five cruises along a transect between Punta Arenas (Chile) and Bremerhaven (Germany), as well as at the coast of Mauretania.
The isotopic source signatures of dissolved H2 extracted from surface water are highly deuterium-depleted and correlate negatively with temperature, showing δD values of (−629 ± 54) ‰ for water temperatures at (27 ± 3) °C and (−249 ± 88) ‰ below (19 ± 1) °C. The results for warmer water masses are consistent with biological production of H2. This is the first time that marine H2 excess has been directly attributed to biological production by isotope measurements. However, the isotope values obtained in the colder water masses indicate that beside possible biological production a significant different source should be considered.
The atmospheric measurements show distinct differences between both hemispheres as well as between seasons. Results from the global chemistry transport model TM5 reproduce the measured H2 mole fractions and isotopic composition well. The climatological global oceanic emissions from the GEMS database are in line with our data and previously published flux calculations. The good agreement between measurements and model results demonstrates that both the magnitude and the isotopic signature of the main components of the marine H2 cycle are in general adequately represented in current atmospheric models despite a proposed source different from biological production or a substantial underestimation of nitrogen fixation by several authors
Finding community structure in very large networks
The discovery and analysis of community structure in networks is a topic of
considerable recent interest within the physics community, but most methods
proposed so far are unsuitable for very large networks because of their
computational cost. Here we present a hierarchical agglomeration algorithm for
detecting community structure which is faster than many competing algorithms:
its running time on a network with n vertices and m edges is O(m d log n) where
d is the depth of the dendrogram describing the community structure. Many
real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in
which case our algorithm runs in essentially linear time, O(n log^2 n). As an
example of the application of this algorithm we use it to analyze a network of
items for sale on the web-site of a large online retailer, items in the network
being linked if they are frequently purchased by the same buyer. The network
has more than 400,000 vertices and 2 million edges. We show that our algorithm
can extract meaningful communities from this network, revealing large-scale
patterns present in the purchasing habits of customers
Structural Examination of Au/Ge(001) by Surface X-Ray Diffraction and Scanning Tunneling Microscopy
The one-dimensional reconstruction of Au/Ge(001) was investigated by means of
autocorrelation functions from surface x-ray diffraction (SXRD) and scanning
tunneling microscopy (STM). Interatomic distances found in the SXRD-Patterson
map are substantiated by results from STM. The Au coverage, recently determined
to be 3/4 of a monolayer of gold, together with SXRD leads to three
non-equivalent positions for Au within the c(8x2) unit cell. Combined with
structural information from STM topography and line profiling, two building
blocks are identified: Au-Ge hetero-dimers within the top wire architecture and
Au homo-dimers within the trenches. The incorporation of both components is
discussed using density functional theory and model based Patterson maps by
substituting Germanium atoms of the reconstructed Ge(001) surface.Comment: 5 pages, 3 figure
A measure of centrality based on the spectrum of the Laplacian
We introduce a family of new centralities, the k-spectral centralities.
k-Spectral centrality is a measurement of importance with respect to the
deformation of the graph Laplacian associated with the graph. Due to this
connection, k-spectral centralities have various interpretations in terms of
spectrally determined information.
We explore this centrality in the context of several examples. While for
sparse unweighted networks 1-spectral centrality behaves similarly to other
standard centralities, for dense weighted networks they show different
properties. In summary, the k-spectral centralities provide a novel and useful
measurement of relevance (for single network elements as well as whole
subnetworks) distinct from other known measures.Comment: 12 pages, 6 figures, 2 table
Dynamic Computation of Network Statistics via Updating Schema
In this paper we derive an updating scheme for calculating some important
network statistics such as degree, clustering coefficient, etc., aiming at
reduce the amount of computation needed to track the evolving behavior of large
networks; and more importantly, to provide efficient methods for potential use
of modeling the evolution of networks. Using the updating scheme, the network
statistics can be computed and updated easily and much faster than
re-calculating each time for large evolving networks. The update formula can
also be used to determine which edge/node will lead to the extremal change of
network statistics, providing a way of predicting or designing evolution rule
of networks.Comment: 17 pages, 6 figure
Optical study of superconducting Ga-rich layers in silicon
We performed phase-sensitive terahertz (0.12 - 1.2 THz) transmission
measurements of Ga-enriched layers in silicon. Below the superconducting
transition, T_{c} = 6.7 K, we find clear signatures of the formation of a
superconducting condensate and of the opening of an energy gap in the optical
spectra. The London penetration depth, \lambda(T), and the condensate density,
n_{s} = \lambda^{2} 0)/\lambda^{2}(T), as functions of temperature demonstrate
behavior, typical for conventional superconductors with \lambda(0) = 1.8 \mu m.
The terahertz spectra can be well described within the framework of Eliashberg
theory with strong electron-phonon coupling: the zero-temperature energy gap is
2\Delta(0) = 2.64 meV and 2\Delta(0)/k_{B}T_{c} = 4.6 \pm 0.1, consistent with
the amorphous state of Ga. At temperatures just above T_{c}, the optical
spectra demonstrate Drude behavior.Comment: 5 pages, 4 figure
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