scholarly journals A Weighted Multi-Local-World Network Evolving Model and Its Application in Software Network Modeling

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Zengyang Li ◽  
Hui Liu ◽  
Jun-An Lu ◽  
Bing Li
Keyword(s):  
Power Law ◽  
Network Modeling ◽  
Real Life ◽  
Strength Distribution ◽  
Software Systems ◽  
Distribution Analysis ◽  
Power Law Distribution ◽  
Software Network ◽  
Evolving Model ◽  
Theoretical Results

The phenomenon of local worlds (also known as communities) exists in numerous real-life networks, for example, computer networks and social networks. We proposed the Weighted Multi-Local-World (WMLW) network evolving model, taking into account (1) the dense links between nodes in a local world, (2) the sparse links between nodes from different local worlds, and (3) the different importance between intra-local-world links and inter-local-world links. On topology evolving, new links between existing local worlds and new local worlds are added to the network, while new nodes and links are added to existing local worlds. On weighting mechanism, weight of links in a local world and weight of links between different local worlds are endowed different meanings. It is theoretically proven that the strength distribution of the generated network by the WMLW model yields to a power-law distribution. Simulations show the correctness of the theoretical results. Meanwhile, the degree distribution also follows a power-law distribution. Analysis and simulation results show that the proposed WMLW model can be used to model the evolution of class diagrams of software systems.

Topology properties of a weighted multi-local-world evolving network

2015 ◽  
Vol 93 (3) ◽  
pp. 353-360 ◽  
Author(s):  
Meifeng Dai ◽  
Danping Zhang ◽  
Lei Li
Keyword(s):  
Real World ◽  
Weight Distribution ◽  
Preferential Attachment ◽  
Mean Field ◽  
Field Method ◽  
Strength Distribution ◽  
Power Law Distribution ◽  
World Model ◽  
The Mean ◽  
Theoretical Results

Many real-world networks, ranging from the world trade web to the Internet network, have been described by multi-local-worlds. It is obvious that the nodes within a local world are much more connected to each other than to the others outside the local world. A multi-local-world model can capture and describe these real-world networks’ topological properties. Based on the local-world model, a weighted multi-local-world evolving network model is presented. This model combines selected nodes with preferential attachment and three kinds of local changes of weights. Using a rate equation and the mean-field method, we study the network’s properties: the weight distribution and the strength distribution. We theoretically prove that the weight distribution and the strength distribution follow a power-law distribution in some conditions. Numerical simulations are in agreement with the theoretical results.

scholarly journals Evolving Model for the Complex Traffic and Transportation Network Considering Self-Growth Situation

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Zhang ◽  
Di Xu
Keyword(s):  
Power Law ◽  
Transportation Network ◽  
The Self ◽  
Node Degree ◽  
Power Law Distribution ◽  
Scale Free ◽  
Scaling Parameter ◽  
Growth Phenomenon ◽  
Airline Network ◽  
Evolving Model

It has been approved that the scale-free feature exists in various complex networks, such as the internet, the cell or the biological networks. In order to analyze the influence of the self-growth phenomenon during the growth on the structure of traffic and transportation network, we formulated an evolving model. Based on the evolving model, we prove in mathematics that, even that the self-growth situation happened, the traffic and transportation network owns the scale-free feature due to that the node degree follows a power-law distribution. A real traffic and transportation network, China domestic airline network is tested to consolidate our conclusions. We find that the airline network has a node degree distribution equivalent to the power-law of which the estimated scaling parameter is about 3.0. Moreover the standard error of the estimated scaling parameter changes according to the self-growth probability. Our findings could provide useful information for determining the optimal structure or status of the traffic and transportation network.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

scholarly journals Power-law behavior of transcription factor dynamics at the single-molecule level implies a continuum affinity model

2021 ◽  
Author(s):  
David A Garcia ◽  
Gregory Fettweis ◽  
Diego M Presman ◽  
Ville Paakinaho ◽  
Christopher Jarzynski ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Single Molecule ◽  
Power Law ◽  
Dwell Time ◽  
Specific Binding ◽  
Power Law Distribution ◽  
Single Molecule Level ◽  
Binding Behavior ◽  
Chromatin Template ◽  
Nuclear Domains

Abstract Single-molecule tracking (SMT) allows the study of transcription factor (TF) dynamics in the nucleus, giving important information regarding the diffusion and binding behavior of these proteins in the nuclear environment. Dwell time distributions obtained by SMT for most TFs appear to follow bi-exponential behavior. This has been ascribed to two discrete populations of TFs—one non-specifically bound to chromatin and another specifically bound to target sites, as implied by decades of biochemical studies. However, emerging studies suggest alternate models for dwell-time distributions, indicating the existence of more than two populations of TFs (multi-exponential distribution), or even the absence of discrete states altogether (power-law distribution). Here, we present an analytical pipeline to evaluate which model best explains SMT data. We find that a broad spectrum of TFs (including glucocorticoid receptor, oestrogen receptor, FOXA1, CTCF) follow a power-law distribution of dwell-times, blurring the temporal line between non-specific and specific binding, suggesting that productive binding may involve longer binding events than previously believed. From these observations, we propose a continuum of affinities model to explain TF dynamics, that is consistent with complex interactions of TFs with multiple nuclear domains as well as binding and searching on the chromatin template.

Some misinterpretations and lack of understanding in differential operators with no singular kernels

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 594-612 ◽  
Author(s):  
Abdon Atangana ◽  
Emile Franc Doungmo Goufo
Keyword(s):  
Fractional Calculus ◽  
Power Law ◽  
Initial Value Problems ◽  
Differential Operators ◽  
Integral Operators ◽  
Initial Value ◽  
Complex Processes ◽  
Singular Kernels ◽  
Fractional Differential ◽  
Theoretical Results

AbstractHumans are part of nature, and as nature existed before mankind, mathematics was created by humans with the main aim to analyze, understand and predict behaviors observed in nature. However, besides this aspect, mathematicians have introduced some laws helping them to obtain some theoretical results that may not have physical meaning or even a representation in nature. This is also the case in the field of fractional calculus in which the main aim was to capture more complex processes observed in nature. Some laws were imposed and some operators were misused, such as, for example, the Riemann–Liouville and Caputo derivatives that are power-law-based derivatives and have been used to model problems with no power law process. To solve this problem, new differential operators depicting different processes were introduced. This article aims to clarify some misunderstandings about the use of fractional differential and integral operators with non-singular kernels. Additionally, we suggest some numerical discretizations for the new differential operators to be used when dealing with initial value problems. Applications of some nature processes are provided.

A Software Network Model for Software Structure and Faults Distribution Analysis

2019 ◽  
Vol 68 (3) ◽  
pp. 844-858 ◽  
Author(s):  
Jun Ai ◽  
Wenzhu Su ◽  
Shaoxiong Zhang ◽  
Yiwen Yang
Keyword(s):  
Network Model ◽  
Distribution Analysis ◽  
Software Structure ◽  
Software Network

scholarly journals Explaining the power-law distribution of human mobility through transportationmodality decomposition

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Kai Zhao ◽  
Mirco Musolesi ◽  
Pan Hui ◽  
Weixiong Rao ◽  
Sasu Tarkoma
Keyword(s):  
Power Law ◽  
Human Mobility ◽  
Power Law Distribution

MASS FUNCTION OF HALOS: A NEW ANALYTICAL APPROACH

2004 ◽  
Vol 13 (07) ◽  
pp. 1345-1349 ◽  
Author(s):  
JOSÉ A. S. LIMA ◽  
LUCIO MARASSI
Keyword(s):  
Power Law ◽  
Free Parameter ◽  
Analytical Approach ◽  
Mass Function ◽  
New Method ◽  
Power Law Distribution ◽  
The Press ◽  
The Universe ◽  
Special Case

A generalization of the Press–Schechter (PS) formalism yielding the mass function of bound structures in the Universe is given. The extended formula is based on a power law distribution which encompasses the Gaussian PS formula as a special case. The new method keeps the original analytical simplicity of the PS approach and also solves naturally its main difficult (the missing factor 2) for a given value of the free parameter.

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