7,938 research outputs found
Walks on Apollonian networks
We carry out comparative studies of random walks on deterministic Apollonian
networks (DANs) and random Apollonian networks (RANs). We perform computer
simulations for the mean first passage time, the average return time, the
mean-square displacement, and the network coverage for unrestricted random
walk. The diffusions both on DANs and RANs are proved to be sublinear. The
search efficiency for walks with various strategies and the influence of the
topology of underlying networks on the dynamics of walks are discussed.
Contrary to one's intuition, it is shown that the self-avoiding random walk,
which has been verified as an optimal strategy for searching on scale-free and
small-world networks, is not the best strategy for the DAN in the thermodynamic
limit.Comment: 5 pages, 4 figure
A Unified Framework for the Pareto Law and Matthew Effect using Scale-Free Networks
We investigate the accumulated wealth distribution by adopting evolutionary
games taking place on scale-free networks. The system self-organizes to a
critical Pareto distribution (1897) of wealth with (which is in agreement with that of U.S. or Japan). Particularly, the
agent's personal wealth is proportional to its number of contacts
(connectivity), and this leads to the phenomenon that the rich gets richer and
the poor gets relatively poorer, which is consistent with the Matthew Effect
present in society, economy, science and so on. Though our model is simple, it
provides a good representation of cooperation and profit accumulation behavior
in economy, and it combines the network theory with econophysics.Comment: 5 pages, 8 figure
Urban traffic from the perspective of dual graph
In this paper, urban traffic is modeled using dual graph representation of
urban transportation network where roads are mapped to nodes and intersections
are mapped to links. The proposed model considers both the navigation of
vehicles on the network and the motion of vehicles along roads. The road's
capacity and the vehicle-turning ability at intersections are naturally
incorporated in the model. The overall capacity of the system can be quantified
by a phase transition from free flow to congestion. Simulation results show
that the system's capacity depends greatly on the topology of transportation
networks. In general, a well-planned grid can hold more vehicles and its
overall capacity is much larger than that of a growing scale-free network.Comment: 7 pages, 10 figure
On the complexity of color-avoiding site and bond percolation
The mathematical analysis of robustness and error-tolerance of complex
networks has been in the center of research interest. On the other hand, little
work has been done when the attack-tolerance of the vertices or edges are not
independent but certain classes of vertices or edges share a mutual
vulnerability. In this study, we consider a graph and we assign colors to the
vertices or edges, where the color-classes correspond to the shared
vulnerabilities. An important problem is to find robustly connected vertex
sets: nodes that remain connected to each other by paths providing any type of
error (i.e. erasing any vertices or edges of the given color). This is also
known as color-avoiding percolation. In this paper, we study various possible
modeling approaches of shared vulnerabilities, we analyze the computational
complexity of finding the robustly (color-avoiding) connected components. We
find that the presented approaches differ significantly regarding their
complexity.Comment: 14 page
Long-term monitoring of the TeV emission from Mrk 421 with the ARGO-YBJ experiment
ARGO-YBJ is an air shower detector array with a fully covered layer of
resistive plate chambers. It is operated with a high duty cycle and a large
field of view. It continuously monitors the northern sky at energies above 0.3
TeV. In this paper, we report a long-term monitoring of Mrk 421 over the period
from 2007 November to 2010 February. This source was observed by the
satellite-borne experiments Rossi X-ray Timing Explorer and Swift in the X-ray
band. Mrk 421 was especially active in the first half of 2008. Many flares are
observed in both X-ray and gamma-ray bands simultaneously. The gamma-ray flux
observed by ARGO-YBJ has a clear correlation with the X-ray flux. No lag
between the X-ray and gamma-ray photons longer than 1 day is found. The
evolution of the spectral energy distribution is investigated by measuring
spectral indices at four different flux levels. Hardening of the spectra is
observed in both X-ray and gamma-ray bands. The gamma-ray flux increases
quadratically with the simultaneously measured X-ray flux. All these
observational results strongly favor the synchrotron self-Compton process as
the underlying radiative mechanism.Comment: 30 pages, 8 figure
Search for Gamma Ray Bursts with the Argo-YBJ Detector in Scaler Mode
We report on the search for Gamma Ray Bursts (GRBs) in the energy range 1-100
GeV in coincidence with the prompt emission detected by satellites using the
Astrophysical Radiation with Ground-based Observatory at YangBaJing (ARGO-YBJ)
air shower detector. Thanks to its mountain location (Yangbajing, Tibet, P.R.
China, 4300 m a.s.l.), active surface (about 6700 m**2 of Resistive Plate
Chambers), and large field of view (about 2 sr, limited only by the atmospheric
absorption), the ARGO-YBJ air shower detector is particularly suitable for the
detection of unpredictable and short duration events such as GRBs. The search
is carried out using the "single particle technique", i.e. counting all the
particles hitting the detector without measurement of the energy and arrival
direction of the primary gamma rays.
Between 2004 December 17 and 2009 April 7, 81 GRBs detected by satellites
occurred within the field of view of ARGO-YBJ (zenith angle < 45 deg). It was
possible to examine 62 of these for >1 GeV counterpart in the ARGO-YBJ data
finding no statistically significant emission. With a lack of detected spectra
in this energy range fluence upper limits are profitable, especially when the
redshift is known and the correction for the extragalactic absorption can be
considered. The obtained fluence upper limits reach values as low as 10**{-5}
erg cm**{-2} in the 1-100 GeV energy region.
Besides this individual search for a higher energy counterpart, a statistical
study of the stack of all the GRBs both in time and in phase was made, looking
for a common feature in the GRB high energy emission. No significant signal has
been detected.Comment: accepted for publication in Ap
Discovering universal statistical laws of complex networks
Different network models have been suggested for the topology underlying
complex interactions in natural systems. These models are aimed at replicating
specific statistical features encountered in real-world networks. However, it
is rarely considered to which degree the results obtained for one particular
network class can be extrapolated to real-world networks. We address this issue
by comparing different classical and more recently developed network models
with respect to their generalisation power, which we identify with large
structural variability and absence of constraints imposed by the construction
scheme. After having identified the most variable networks, we address the
issue of which constraints are common to all network classes and are thus
suitable candidates for being generic statistical laws of complex networks. In
fact, we find that generic, not model-related dependencies between different
network characteristics do exist. This allows, for instance, to infer global
features from local ones using regression models trained on networks with high
generalisation power. Our results confirm and extend previous findings
regarding the synchronisation properties of neural networks. Our method seems
especially relevant for large networks, which are difficult to map completely,
like the neural networks in the brain. The structure of such large networks
cannot be fully sampled with the present technology. Our approach provides a
method to estimate global properties of under-sampled networks with good
approximation. Finally, we demonstrate on three different data sets (C.
elegans' neuronal network, R. prowazekii's metabolic network, and a network of
synonyms extracted from Roget's Thesaurus) that real-world networks have
statistical relations compatible with those obtained using regression models
Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas
Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI).
Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model.
Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen.
Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas
Small World Network of Athletes: Graph Representation of the World Professional Tennis Player
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
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