26,584 research outputs found
Locating influential nodes via dynamics-sensitive centrality
With great theoretical and practical significance, locating influential nodes
of complex networks is a promising issues. In this paper, we propose a
dynamics-sensitive (DS) centrality that integrates topological features and
dynamical properties. The DS centrality can be directly applied in locating
influential spreaders. According to the empirical results on four real networks
for both susceptible-infected-recovered (SIR) and susceptible-infected (SI)
spreading models, the DS centrality is much more accurate than degree,
-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
MEDUSA - New Model of Internet Topology Using k-shell Decomposition
The k-shell decomposition of a random graph provides a different and more
insightful separation of the roles of the different nodes in such a graph than
does the usual analysis in terms of node degrees. We develop this approach in
order to analyze the Internet's structure at a coarse level, that of the
"Autonomous Systems" or ASes, the subnetworks out of which the Internet is
assembled. We employ new data from DIMES (see http://www.netdimes.org), a
distributed agent-based mapping effort which at present has attracted over 3800
volunteers running more than 7300 DIMES clients in over 85 countries. We
combine this data with the AS graph information available from the RouteViews
project at Univ. Oregon, and have obtained an Internet map with far more detail
than any previous effort.
The data suggests a new picture of the AS-graph structure, which
distinguishes a relatively large, redundantly connected core of nearly 100 ASes
and two components that flow data in and out from this core. One component is
fractally interconnected through peer links; the second makes direct
connections to the core only. The model which results has superficial
similarities with and important differences from the "Jellyfish" structure
proposed by Tauro et al., so we call it a "Medusa." We plan to use this picture
as a framework for measuring and extrapolating changes in the Internet's
physical structure. Our k-shell analysis may also be relevant for estimating
the function of nodes in the "scale-free" graphs extracted from other
naturally-occurring processes.Comment: 24 pages, 17 figure
Rapid Computation of Thermodynamic Properties Over Multidimensional Nonbonded Parameter Spaces using Adaptive Multistate Reweighting
We show how thermodynamic properties of molecular models can be computed over
a large, multidimensional parameter space by combining multistate reweighting
analysis with a linear basis function approach. This approach reduces the
computational cost to estimate thermodynamic properties from molecular
simulations for over 130,000 tested parameter combinations from over a thousand
CPU years to tens of CPU days. This speed increase is achieved primarily by
computing the potential energy as a linear combination of basis functions,
computed from either modified simulation code or as the difference of energy
between two reference states, which can be done without any simulation code
modification. The thermodynamic properties are then estimated with the
Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model
parameters without the need to define a priori how the states are connected by
a pathway. Instead, we adaptively sample a set of points in parameter space to
create mutual configuration space overlap. The existence of regions of poor
configuration space overlap are detected by analyzing the eigenvalues of the
sampled states' overlap matrix. The configuration space overlap to sampled
states is monitored alongside the mean and maximum uncertainty to determine
convergence, as neither the uncertainty or the configuration space overlap
alone is a sufficient metric of convergence.
This adaptive sampling scheme is demonstrated by estimating with high
precision the solvation free energies of charged particles of Lennard-Jones
plus Coulomb functional form. We also compute entropy, enthalpy, and radial
distribution functions of unsampled parameter combinations using only the data
from these sampled states and use the free energies estimates to examine the
deviation of simulations from the Born approximation to the solvation free
energy
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