scholarly journals STRENGTH DISTRIBUTION IN DERIVATIVE NETWORKS

2005 ◽  
Vol 16 (07) ◽  
pp. 1097-1105 ◽  
Author(s):  
LUCIANO DA FONTOURA COSTA ◽  
GONZALO TRAVIESO
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Lower Limit ◽  
Network Model ◽  
Power Law ◽  
Neuronal Networks ◽  
Strength Distribution ◽  
Node Degree ◽  
Upper Limit ◽  
The Difference

This article describes a complex network model whose weights are proportional to the difference between uniformly distributed "fitness" values assigned to the nodes. It is shown both analytically and experimentally that the strength density (i.e., the weighted node degree) for this model, called derivative complex networks, follows a power law with exponent γ<1 if the fitness has an upper limit and γ>1 if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics are also discussed.

Link prediction based on contribution of neighbors

2020 ◽  
Vol 31 (11) ◽  
pp. 2050158
Author(s):  
Xiang-Chun Liu ◽  
Dian-Qing Meng ◽  
Xu-Zhen Zhu ◽  
Yang Tian
Keyword(s):  
Complex Network ◽  
Link Prediction ◽  
Node Degree ◽  
Prediction Methods ◽  
Obvious Advantage ◽  
Node Similarity ◽  
The Difference

Link prediction based on node similarity has become one of the most effective prediction methods for complex network. When calculating the similarity between two unconnected endpoints in link prediction, most scholars evaluate the influence of endpoint based on the node degree. However, this method ignores the difference in contribution of neighbor (NC) nodes for endpoint. Through abundant investigations and analyses, the paper quantifies the NC nodes to endpoint, and conceives NC Index to evaluate the endpoint influence accurately. Extensive experiments on 12 real datasets indicate that our proposed algorithm can increase the accuracy of link prediction significantly and show an obvious advantage over traditional algorithms.

scholarly journals Power-law distribution of degree–degree distance: A better representation of the scale-free property of complex networks

2020 ◽  
Vol 117 (26) ◽  
pp. 14812-14818 ◽  
Author(s):  
Bin Zhou ◽  
Xiangyi Meng ◽  
H. Eugene Stanley
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Power Law ◽  
Real World ◽  
Preferential Attachment ◽  
Statistical Tests ◽  
Finite Size ◽  
Distance Distribution ◽  
Scale Free ◽  
Degree Distance

Whether real-world complex networks are scale free or not has long been controversial. Recently, in Broido and Clauset [A. D. Broido, A. Clauset,Nat. Commun.10, 1017 (2019)], it was claimed that the degree distributions of real-world networks are rarely power law under statistical tests. Here, we attempt to address this issue by defining a fundamental property possessed by each link, the degree–degree distance, the distribution of which also shows signs of being power law by our empirical study. Surprisingly, although full-range statistical tests show that degree distributions are not often power law in real-world networks, we find that in more than half of the cases the degree–degree distance distributions can still be described by power laws. To explain these findings, we introduce a bidirectional preferential selection model where the link configuration is a randomly weighted, two-way selection process. The model does not always produce solid power-law distributions but predicts that the degree–degree distance distribution exhibits stronger power-law behavior than the degree distribution of a finite-size network, especially when the network is dense. We test the strength of our model and its predictive power by examining how real-world networks evolve into an overly dense stage and how the corresponding distributions change. We propose that being scale free is a property of a complex network that should be determined by its underlying mechanism (e.g., preferential attachment) rather than by apparent distribution statistics of finite size. We thus conclude that the degree–degree distance distribution better represents the scale-free property of a complex network.

scholarly journals Synchronization in a Novel Local-World Dynamical Network Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianeng Tang ◽  
Peizhong Liu
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Optimal Growth ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Network Research ◽  
Topology Model ◽  
The Relationship ◽  
Random Failures

Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes.

On the Proper Description of Complex Network Dynamics

2018 ◽  
Author(s):  
Chun-Lin Yang ◽  
C. Steve Suh
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Collective Behavior ◽  
Local Level ◽  
Network Dynamics ◽  
Global Dynamics ◽  
Statistical Entropy ◽  
Network Systems ◽  
Local Dynamics ◽  
The Difference

Real-world networks are dynamical complex network systems. The dynamics of a network system is a coupling of the local dynamics with the global dynamics. The local dynamics is the time-varying behaviors of ensembles at the local level. The global dynamics is the collective behavior of the ensembles following specific laws at the global level. These laws include basic physical principles and constraints. Complex networks have inherent resilience that offsets disturbance and maintains the state of the system. However, when disturbance is potent enough, network dynamics can be perturbed to a level that ensembles no longer follow the constraint conditions. As a result, the collective behavior of a complex network diminishes and the network collapses. The characteristic of a complex network is the response of the system which is time-dependent. Therefore, complex networks need to account for time-dependency and obey physical laws and constraints. Statistical mechanics is viable for the study of multi-body dynamic systems having uncertain states such as complex network systems. Statistical entropy can be used to define the distribution of the states of ensembles. The difference between the states of ensembles define the interaction between them. This interaction is known as the collective behavior. In other words statistical entropy defines the dynamics of a complex network. Variation of entropy corresponds to the variation of network dynamics and vice versa. Therefore, entropy can serve as an indicator of network dynamics. A stable network is characterized by a specific entropy while a network on the verge of collapse is characterized by another. As the collective behavior of a complex network can be described by entropy, the correlation between the statistical entropy and network dynamics is investigated.

Building Complex Network Similar to Facebook

2014 ◽  
Vol 513-517 ◽  
pp. 909-913
Author(s):  
Dong Wei Guo ◽  
Xiang Yan Meng ◽  
Cai Fang Hou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Clustering Coefficient ◽  
Network Characteristics ◽  
Modeling Methods ◽  
Community Strength ◽  
Strength Based

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

scholarly journals Exponential Synchronization Analysis and Control for Discrete-Time Uncertain Delay Complex Networks with Stochastic Effects

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Tianbo Wang ◽  
Wuneng Zhou ◽  
Dejun Zhao ◽  
Shouwei Zhao
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Discrete Time ◽  
Lyapunov Stability Theory ◽  
Exponential Synchronization ◽  
Control Methods ◽  
Stochastic Effects ◽  
Inequality Technique ◽  
The Difference ◽  
And Control

The exponential synchronization for a class of discrete-time uncertain complex networks with stochastic effects and time delay is investigated by using the Lyapunov stability theory and discrete Halanay inequality. The uncertainty arises from the difference of the nodes’ reliability in the complex network. Through constructing an appropriate Lyapunov function and applying inequality technique, some synchronization criteria and two control methods are obtained to ensure the considered complex network being exponential synchronization. Finally, a numerical example is provided to show the effectiveness of our proposed methods.

scholarly journals Non-contact infrared thermometers compared with current approaches in primary care for children aged 5 years and under: a method comparison study

2020 ◽  
Vol 24 (53) ◽  
pp. 1-28 ◽  
Author(s):  
Ann Van den Bruel ◽  
Jan Verbakel ◽  
Kay Wang ◽  
Susannah Fleming ◽  
Gea Holtman ◽  
...  
Keyword(s):  
Primary Care ◽  
Confidence Interval ◽  
Lower Limit ◽  
Method Comparison ◽  
Health Technology ◽  
Comparison Study ◽  
Infrared Thermometer ◽  
Upper Limit ◽  
The Difference ◽  
Method Comparison Study

Background Current options for temperature measurement in children presenting to primary care include either electronic axillary or infrared tympanic thermometers. Non-contact infrared thermometers could reduce both the distress of the child and the risk of cross-infection. Objectives The objective of this study was to compare the use of non-contact thermometers with the use of electronic axillary and infrared tympanic thermometers in children presenting to primary care. Design Method comparison study with a nested qualitative study. Setting Primary care in Oxfordshire. Participants Children aged ≤ 5 years attending with an acute illness. Interventions Two types of non-contact infrared thermometers [i.e. Thermofocus (Tecnimed, Varese, Italy) and Firhealth (Firhealth, Shenzhen, China)] were compared with an electronic axillary thermometer and an infrared tympanic thermometer. Main outcome measures The primary outcome was agreement between the Thermofocus non-contact infrared thermometer and the axillary thermometer. Secondary outcomes included agreement between all other sets of thermometers, diagnostic accuracy for detecting fever, parental and child ratings of acceptability and discomfort, and themes arising from our qualitative interviews with parents. Results A total of 401 children (203 boys) were recruited, with a median age of 1.6 years (interquartile range 0.79–3.38 years). The readings of the Thermofocus non-contact infrared thermometer differed from those of the axillary thermometer by –0.14 °C (95% confidence interval –0.21 to –0.06 °C) on average with the lower limit of agreement being –1.57 °C (95% confidence interval –1.69 to –1.44 °C) and the upper limit being 1.29 °C (95% confidence interval 1.16 to 1.42 °C). The readings of the Firhealth non-contact infrared thermometer differed from those of the axillary thermometer by –0.16 °C (95% confidence interval –0.23 to –0.09 °C) on average, with the lower limit of agreement being –1.54 °C (95% confidence interval –1.66 to –1.41 °C) and the upper limit being 1.22 °C (95% confidence interval 1.10 to 1.34 °C). The difference between the first and second readings of the Thermofocus was –0.04 °C (95% confidence interval –0.07 to –0.01 °C); the lower limit was –0.56 °C (95% confidence interval –0.60 to –0.51 °C) and the upper limit was 0.47 °C (95% confidence interval 0.43 to 0.52 °C). The difference between the first and second readings of the Firhealth thermometer was 0.01 °C (95% confidence interval –0.02 to 0.04 °C); the lower limit was –0.60 °C (95% confidence interval –0.65 to –0.54 °C) and the upper limit was 0.61 °C (95% confidence interval 0.56 to 0.67 °C). Sensitivity and specificity for the Thermofocus non-contact infrared thermometer were 66.7% (95% confidence interval 38.4% to 88.2%) and 98.0% (95% confidence interval 96.0% to 99.2%), respectively. For the Firhealth non-contact infrared thermometer, sensitivity was 12.5% (95% confidence interval 1.6% to 38.3%) and specificity was 99.4% (95% confidence interval 98.0% to 99.9%). The majority of parents found all methods to be acceptable, although discomfort ratings were highest for the axillary thermometer. The non-contact thermometers required fewer readings than the comparator thermometers. Limitations A method comparison study does not compare new methods against a reference standard, which in this case would be central thermometry requiring the placement of a central line, which is not feasible or acceptable in primary care. Electronic axillary and infrared tympanic thermometers have been found to have moderate agreement themselves with central temperature measurements. Conclusions The 95% limits of agreement are > 1 °C for both non-contact infrared thermometers compared with electronic axillary and infrared tympanic thermometers, which could affect clinical decision-making. Sensitivity for fever was low to moderate for both non-contact thermometers. Future work Better methods for peripheral temperature measurement that agree well with central thermometry are needed. Trial registration Current Controlled Trials ISRCTN15413321. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 53. See the NIHR Journals Library website for further project information.

scholarly journals Distribution of Node Characteristics in Evolving Tripartite Network

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 263 ◽  
Author(s):  
Ladislav Beranek ◽  
Radim Remes
Keyword(s):  
Network Model ◽  
Power Law ◽  
Real World ◽  
Strength Distribution ◽  
Growth Mechanisms ◽  
Preferential Growth ◽  
Basic Properties ◽  
And Behavior

Many real-world networks have a natural tripartite structure. Investigating the structure and the behavior of actors in these networks is useful to gain a deeper understanding of their behavior and dynamics. In our paper, we describe an evolving tripartite network using a network model with preferential growth mechanisms and different rules for changing the strength of nodes and the weights of edges. We analyze the characteristics of the strength distribution and behavior of selected nodes and selected actors in this tripartite network. The distributions of these analyzed characteristics follow the power-law under different modeled conditions. Performed simulations have confirmed all these results. Despite its simplicity, the model expresses well the basic properties of the modeled network. It can provide further insights into the behavior of systems with more complex behaviors, such as the multi-actor e-commerce system that we have used as a real basis for the validation of our model.

A new method to identify influential nodes based on combining of existing centrality measures

2017 ◽  
Vol 31 (26) ◽  
pp. 1750243 ◽  
Author(s):  
Liguo Fei ◽  
Hongming Mo ◽  
Yong Deng
Keyword(s):  
Complex Networks ◽  
Lower Limit ◽  
Information Flow ◽  
New Method ◽  
Centrality Measures ◽  
Centrality Measure ◽  
Upper Limit ◽  
Open Issue ◽  
Novel Method ◽  
Influential Nodes

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments.

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