31,195 research outputs found
A paradox in community detection
Recent research has shown that virtually all algorithms aimed at the
identification of communities in networks are affected by the same main
limitation: the impossibility to detect communities, even when these are
well-defined, if the average value of the difference between internal and
external node degrees does not exceed a strictly positive value, in literature
known as detectability threshold. Here, we counterintuitively show that the
value of this threshold is inversely proportional to the intrinsic quality of
communities: the detection of well-defined modules is thus more difficult than
the identification of ill-defined communities.Comment: 5 pages, 3 figure
Angular Normal Modes of a Circular Coulomb Cluster
We investigate the angular normal modes for small oscillations about an
equilibrium of a single-component coulomb cluster confined by a radially
symmetric external potential to a circle. The dynamical matrix for this system
is a Laplacian symmetrically circulant matrix and this result leads to an
analytic solution for the eigenfrequencies of the angular normal modes. We also
show the limiting dependence of the largest eigenfrequency for large numbers of
particles
A Turbo-Detection Aided Serially Concatenated MPEG-4/TCM Videophone Transceiver
A Turbo-detection aided serially concatenated inner Trellis Coded Modulation (TCM) scheme is combined with four different outer codes, namely with a Reversible Variable Length Code (RVLC), a Non-Systematic Convolutional (NSC) code a Recursive Systematic Convolutional (RSC) code or a Low Density Parity Check (LDPC) code. These four outer constituent codes are comparatively studied in the context of an MPEG4 videophone transceiver. These serially concatenated schemes are also compared to a stand-alone LDPC coded MPEG4 videophone system at the same effective overall coding rate. The performance of the proposed schemes is evaluated when communicating over uncorrelated Rayleigh fading channels. It was found that the serially concatenated TCM-NSC scheme was the most attractive one in terms of coding gain and decoding complexity among all the schemes considered in the context of the MPEG4 videophone transceiver. By contrast, the serially concatenated TCM-RSC scheme was found to attain the highest iteration gain among the schemes considered
Computing Diffusion State Distance using Green's Function and Heat Kernel on Graphs
The diffusion state distance (DSD) was introduced by
Cao-Zhang-Park-Daniels-Crovella-Cowen-Hescott [{\em PLoS ONE, 2013}] to capture
functional similarity in protein-protein interaction networks. They proved the
convergence of DSD for non-bipartite graphs. In this paper, we extend the DSD
to bipartite graphs using lazy-random walks and consider the general
-version of DSD. We discovered the connection between the DSD
-distance and Green's function, which was studied by Chung and Yau [{\em
J. Combinatorial Theory (A), 2000}]. Based on that, we computed the DSD
-distance for Paths, Cycles, Hypercubes, as well as random graphs
and . We also examined the DSD distances of two biological
networks.Comment: Accepted by the 11th Workshop on Algorithms and Models for the Web
Graph (WAW2014
Some Exact Results for Spanning Trees on Lattices
For -vertex, -dimensional lattices with , the number
of spanning trees grows asymptotically as
in the thermodynamic limit. We present an exact closed-form result for the
asymptotic growth constant for spanning trees on the
-dimensional body-centered cubic lattice. We also give an exact integral
expression for on the face-centered cubic lattice and an exact
closed-form expression for on the lattice.Comment: 7 pages, 1 tabl
Random Vibrational Networks and Renormalization Group
We consider the properties of vibrational dynamics on random networks, with
random masses and spring constants. The localization properties of the
eigenstates contrast greatly with the Laplacian case on these networks. We
introduce several real-space renormalization techniques which can be used to
describe this dynamics on general networks, drawing on strong disorder
techniques developed for regular lattices. The renormalization group is capable
of elucidating the localization properties, and provides, even for specific
network instances, a fast approximation technique for determining the spectra
which compares well with exact results.Comment: 4 pages, 3 figure
The effect of network structure on phase transitions in queuing networks
Recently, De Martino et al have presented a general framework for the study
of transportation phenomena on complex networks. One of their most significant
achievements was a deeper understanding of the phase transition from the
uncongested to the congested phase at a critical traffic load. In this paper,
we also study phase transition in transportation networks using a discrete time
random walk model. Our aim is to establish a direct connection between the
structure of the graph and the value of the critical traffic load. Applying
spectral graph theory, we show that the original results of De Martino et al
showing that the critical loading depends only on the degree sequence of the
graph -- suggesting that different graphs with the same degree sequence have
the same critical loading if all other circumstances are fixed -- is valid only
if the graph is dense enough. For sparse graphs, higher order corrections,
related to the local structure of the network, appear.Comment: 12 pages, 7 figure
Clustering and the hyperbolic geometry of complex networks
Clustering is a fundamental property of complex networks and it is the
mathematical expression of a ubiquitous phenomenon that arises in various types
of self-organized networks such as biological networks, computer networks or
social networks. In this paper, we consider what is called the global
clustering coefficient of random graphs on the hyperbolic plane. This model of
random graphs was proposed recently by Krioukov et al. as a mathematical model
of complex networks, under the fundamental assumption that hyperbolic geometry
underlies the structure of these networks. We give a rigorous analysis of
clustering and characterize the global clustering coefficient in terms of the
parameters of the model. We show how the global clustering coefficient can be
tuned by these parameters and we give an explicit formula for this function.Comment: 51 pages, 1 figur
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