88 research outputs found
Critical behavior of a stochastic anisotropic Bak-Sneppen model
In this paper we present our study on the critical behavior of a stochastic
anisotropic Bak-Sneppen (saBS) model, in which a parameter is
introduced to describe the interaction strength among nearest species. We
estimate the threshold fitness and the critical exponent by
numerically integrating a master equation for the distribution of avalanche
spatial sizes. Other critical exponents are then evaluated from previously
known scaling relations. The numerical results are in good agreement with the
counterparts yielded by the Monte Carlo simulations. Our results indicate that
all saBS models with nonzero interaction strength exhibit self-organized
criticality, and fall into the same universality class, by sharing the
universal critical exponents.Comment: 9 pages, 7 figures. arXiv admin note: text overlap with
arXiv:cond-mat/9803068 by other author
Community detection by label propagation with compression of flow
The label propagation algorithm (LPA) has been proved to be a fast and
effective method for detecting communities in large complex networks. However,
its performance is subject to the non-stable and trivial solutions of the
problem. In this paper, we propose a modified label propagation algorithm LPAf
to efficiently detect community structures in networks. Instead of the majority
voting rule of the basic LPA, LPAf updates the label of a node by considering
the compression of a description of random walks on a network. A multi-step
greedy agglomerative strategy is employed to enable LPAf to escape the local
optimum. Furthermore, an incomplete update condition is also adopted to speed
up the convergence. Experimental results on both synthetic and real-world
networks confirm the effectiveness of our algorithm
Community Detection in Dynamic Networks via Adaptive Label Propagation
An adaptive label propagation algorithm (ALPA) is proposed to detect and
monitor communities in dynamic networks. Unlike the traditional methods by
re-computing the whole community decomposition after each modification of the
network, ALPA takes into account the information of historical communities and
updates its solution according to the network modifications via a local label
propagation process, which generally affects only a small portion of the
network. This makes it respond to network changes at low computational cost.
The effectiveness of ALPA has been tested on both synthetic and real-world
networks, which shows that it can successfully identify and track dynamic
communities. Moreover, ALPA could detect communities with high quality and
accuracy compared to other methods. Therefore, being low-complexity and
parameter-free, ALPA is a scalable and promising solution for some real-world
applications of community detection in dynamic networks.Comment: 16 pages, 11 figure
The effects of overtaking strategy in the Nagel-Schreckenberg model
Based on the Nagel-Schreckenberg (NS) model with periodic boundary
conditions, we proposed the NSOS model by adding the overtaking strategy (OS).
In our model, overtaking vehicles are randomly selected with probability at
each time step, and the successful overtaking is determined by their
velocities. We observed that (i) traffic jams still occur in the NSOS model;
(ii) OS increases the traffic flow in the regime where the densities exceed the
maximum flow density. We also studied the phase transition (from free flow
phase to jammed phase) of the NSOS model by analyzing the overtaking success
rate, order parameter, relaxation time and correlation function, respectively.
It was shown that the NSOS model differs from the NS model mainly in the jammed
regime, and the influence of OS on the transition density is dominated by the
braking probability Comment: 9 pages, 20 figures, to be published in The European Physical Journal
B (EPJB
The characteristics of cycle-nodes-ratio and its application to network classification
Cycles, which can be found in many different kinds of networks, make the
problems more intractable, especially when dealing with dynamical processes on
networks. On the contrary, tree networks in which no cycle exists, are
simplifications and usually allow for analyticity. There lacks a quantity,
however, to tell the ratio of cycles which determines the extent of network
being close to tree networks. Therefore we introduce the term Cycle Nodes Ratio
(CNR) to describe the ratio of number of nodes belonging to cycles to the
number of total nodes, and provide an algorithm to calculate CNR. CNR is
studied in both network models and real networks. The CNR remains unchanged in
different sized Erd\"os R\'enyi (ER) networks with the same average degree, and
increases with the average degree, which yields a critical turning point. The
approximate analytical solutions of CNR in ER networks are given, which fits
the simulations well. Furthermore, the difference between CNR and two-core
ratio (TCR) is analyzed. The critical phenomenon is explored by analysing the
giant component of networks. We compare the CNR in network models and real
networks, and find the latter is generally smaller. Combining the
coarse-graining method can distinguish the CNR structure of networks with high
average degree. The CNR is also applied to four different kinds of
transportation networks and fungal networks, which give rise to different zones
of effect. It is interesting to see that CNR is very useful in network
recognition of machine learning.Comment: 27 pages,16 figures,3 table
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