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1.
Many realistic networks have community structures, namely, a network
consists of groups of nodes within which links are dense but among
which links are sparse. This paper proposes a growing network model
based on local processes, the addition of new nodes intra-community
and new links intra- or inter-community. Also, it utilizes the
preferential attachment for building connections determined by
nodes' strengths, which evolves dynamically during the growth of the
system. The resulting network reflects the intrinsic community
structure with generalized power-law distributions of nodes' degrees
and strengths. 相似文献
2.
The study of community networks has attracted considerable attention recently. In this paper, we propose an evolving community network model based on local processes, the addition of new nodes intra-community and new links intra- or inter-community. Employing growth and preferential attachment mechanisms, we generate networks with a generalized power-law distribution of nodes’ degrees. 相似文献
3.
Community structure is an important characteristic in real complex network. It is a network consists of groups of nodes within which links are dense but among which links are sparse. In this paper, the evolving network include node, link and community growth and we apply the community size preferential attachment and strength preferential attachment to a growing weighted network model and utilize weight assigning mechanism from BBV model. The resulting network reflects the intrinsic community structure with generalized power-law distributions of nodes' degrees and strengths. 相似文献
4.
This paper studies a simple asymmetrically evolved community
network with a combination of preferential attachment and random
properties. An important issue about community networks is to
discover the different utility increments of two nodes, where the
utility is introduced to investigate the asymmetrical effect of
connecting two nodes. On the other hand, the connection of two nodes
in community networks can be classified as two nodes belonging to the
same or to different communities. The simulation results show that the
model can reproduce a power-law utility distribution P(u)~u-σ, σ = 2 + 1/p, which can be obtained by
using mean-field approximation methods. Furthermore, the model
exhibits exponential behaviour with respect to small values of a
parameter denoting the random effect in our model at the low-utility
region and a power-law feature with respect to big values of this
parameter at the high-utility region, which is in good agreement with
theoretical analysis. This kind of community network can reproduce
a unique utility distribution by theoretical and numerical analysis. 相似文献
5.
Preferential attachment is one possible way to obtain a scale-free network. We develop a self-consistent method to determine whether preferential attachment occurs during the growth of a network, and to extract the preferential attachment rule using time-dependent data. Model networks are grown with known preferential attachment rules to test the method, which is seen to be robust. The method is then applied to a scale-free inherent structure (IS) network, which represents the connections between minima via transition states on a potential energy landscape. Even though this network is static, we can examine the growth of the network as a function of a threshold energy (rather than time), where only those transition states with energies lower than the threshold energy contribute to the network. For these networks we are able to detect the presence of preferential attachment, and this helps to explain the ubiquity of funnels on potential energy landscapes. However, the scale-free degree distribution shows some differences from that of a model network grown using the obtained preferential attachment rules, implying that other factors are also important in the growth process. 相似文献
6.
Many social and biological networks consist of communities–groups of nodes within which links are dense but among which links are sparse. It turns out that most of these networks are best described by weighted networks, whose properties and dynamics depend not only on their structures but also on the link weights among their nodes. Recently, there are considerable interests in the study of properties as well as modelling of such networks with community structures. To our knowledge, however, no study of any weighted network model with such a community structure has been presented in the literature to date. In this paper, we propose a weighted evolving network model with a community structure. The new network model is based on the inner-community and inter-community preferential attachments and preferential strengthening mechanism. Simulation results indicate that this network model indeed reflect the intrinsic community structure, with various power-law distributions of the node degrees, link weights, and node strengths. 相似文献
7.
8.
WU Jian-Jun GAO Zi-You SUN Hui-Jun 《理论物理通讯》2006,46(7)
In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics ofthis new network are given. 相似文献
9.
WU Jian-Jun GAO Zi-You SUN Hui-Jun 《理论物理通讯》2006,46(1):183-186
In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics of this new network are given. 相似文献
10.
Areejit Samal 《Physica A》2009,388(8):1535-1545
We study a model for the evolution of chemical species under a combination of population dynamics on a short time scale, and a selection mechanism on a longer time scale. Least fit nodes are replaced by new nodes whose links are attached to the nodes of the given network via preferential attachment. In contrast to a random attachment of newly incoming nodes that was used in previous work, this preferential attachment mechanism accelerates the generation of a so-called autocatalytic set after a start from a random geometry, and the growth of this structure, until it saturates in a stationary phase in which the whole system is an autocatalytic set. Moreover, the system in the stationary phase becomes much more stable against crashes in the population size as compared to random attachment. We explain in detail, in terms of graph theoretical notions, which structure of the resulting network is responsible for this stability. Essentially it is a very dense core with many loops and less nodes playing the role of a keystone that prevents the system from crashing, almost completely. 相似文献
11.
Inspired by scientific collaboration networks (SCN), especially our empirical analysis of econophysicists network, an evolutionary model for weighted networks is proposed. Besides a new vertex added in at every time step, old vertices can also attempt to build up new links, or to reconnect the existing links. The number of connections repeated between two nodes is converted into the weight of the link. This provides a natural way for the evolution of link weight. The path-dependent preferential attachment mechanism with local information is also introduced. It increases the clustering coefficient of the network significantly. The model shows the scale-free phenomena in degree and vertex weight distribution. It also gives well qualitatively consistent behavior with the empirical results. 相似文献
12.
13.
Preferential attachment is considered one of the key factors in the formation of scale-free networks. However, complete random attachment without a preferential mechanism can also generate scale-free networks in nature, such as protein interaction networks in cells. This article presents a new scale-free network model that applies the following general mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach to random neighbors of random vertices that are already well connected. The proposed model does not require global-based preferential strategies and utilizes only the random attachment method. Theoretical analysis and numerical simulation results denote that the proposed model has steady scale-free network characteristics, and random attachment without a preferential mechanism may generate scale-free networks. 相似文献
14.
In this paper, we propose a simple model that can generate
small-world network with community structure. The network is
introduced as a tunable community organization with parameter r,
which is directly measured by the ratio of inter- to intra-community
connectivity, and a smaller r corresponds to a stronger community
structure. The structure properties, including the degree
distribution, clustering, the communication efficiency and
modularity are also analysed for the network. In addition, by using
the Kuramoto model, we investigated the phase synchronization on
this network, and found that increasing the fuzziness of community
structure will markedly enhance the network synchronizability;
however, in an abnormal region (r ≤ 0.001), the network has even
worse synchronizability than the case of isolated communities (r =
0). Furthermore, this network exhibits a remarkable
synchronization behaviour in topological scales: the oscillators of
high densely interconnected communities synchronize more easily, and
more rapidly than the whole network. 相似文献
15.
Many real networks are characterized by overlapping community structures in which vertices may belong to more than one community. In this paper, we propose a network model with overlapping community structure. The analytical and numerical results show that the connectivity distribution of this network follows a power law. We employ this network to investigate the impact of overlapping community structure on susceptible-infected-susceptible (SIS) epidemic spreading process. The simulation results indicate that significant overlapping community structure results in a major infection prevalence and leads to a peak of the spread velocity in the early stages of the emerging infection. 相似文献
16.
17.
By revisiting the preferential attachment (PA) mechanism for generating a classical scale-free network, we propose a class of novel preferential attachment similarity indices for predicting future links in evolving networks. Extensive experiments on 14 real-life networks show that these new indices can provide more accurate prediction than the traditional one. Due to the improved prediction accuracy and low computational complexity, these proposed preferential attachment indices can be helpful for providing both instructions for mining unknown links and new insights to understand the underlying mechanisms that drive the network evolution. 相似文献
18.
Cascades with coupled map lattices in preferential attachment community networks 总被引:2,自引:0,他引:2 下载免费PDF全文
In this paper, cascading failure is studied by coupled map lattice (CML) methods in preferential attachment community networks. It is found that external perturbation R is increasing with modularity Q growing by simulation. In particular, the large modularity Q can hold off the cascading failure dynamic process in community networks. Furthermore, different attack strategies also greatly affect the cascading failure dynamic process. It is particularly significant to control cascading failure process in real community networks. 相似文献
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20.
Preferential attachment is widely recognised as the principal driving force behind the evolution of many growing networks, and measuring the extent to which it occurs during the growth of a network is important for explaining its overall structure. Conventional methods require that the timeline of a growing network is known, that is, the order in which the nodes of the network appeared in time is available. But growing network datasets are commonly accompanied by missing-timelines, in which instance the order of the nodes in time cannot be readily ascertained from the data. To address this shortcoming, we propose a Markov chain Monte Carlo algorithm for measuring preferential attachment in growing networks with missing-timelines. Key to our approach is that any growing network model gives rise to a probability distribution over the space of networks. This enables a growing network model to be fitted to a growing network dataset with missing-timeline, allowing not only for the prevalence of preferential attachment to be estimated as a model parameter, but the timeline also. Parameter estimation is achieved by implementing a novel Metropolis–Hastings sampling scheme for updating both the preferential attachment parameter and timeline. A simulation study demonstrates that our method accurately measures the occurrence of preferential attachment in networks generated according to the underlying model. What is more, our approach is illustrated on a small sub-network of the United States patent citation network. Since the timeline for this example is in fact known, we are able to validate our approach against the conventional methods, showing that they give mutually consistent estimates. 相似文献