scholarly journals A toward cost-effective scale-free coupling network construction method

2016 ◽  
Vol 65 (9) ◽  
pp. 098901
Author(s):  
Jin Xue-Guang ◽  
Shou Guo-Chu ◽  
Hu Yi-Hong ◽  
Guo Zhi-Gang
Keyword(s):  
Cost Effective ◽  
Construction Method ◽  
Network Construction ◽  
Scale Free

scholarly journals A Novel Information Theoretical Criterion for Climate Network Construction

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1500
Author(s):  
Sara Cornejo-Bueno ◽  
Mihaela I. Chidean ◽  
Antonio J. Caamaño ◽  
Luis Prieto-Godino ◽  
Sancho Salcedo-Sanz
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Construction Method ◽  
Membership Probability ◽  
Network Construction ◽  
Wind Speed Prediction ◽  
Emergent Behaviour ◽  
Symmetry Properties ◽  
Leibler Divergence ◽  
Speed Prediction

This paper presents a novel methodology for Climate Network (CN) construction based on the Kullback-Leibler divergence (KLD) among Membership Probability (MP) distributions, obtained from the Second Order Data-Coupled Clustering (SODCC) algorithm. The proposed method is able to obtain CNs with emergent behaviour adapted to the variables being analyzed, and with a low number of spurious or missing links. We evaluate the proposed method in a problem of CN construction to assess differences in wind speed prediction at different wind farms in Spain. The considered problem presents strong local and mesoscale relationships, but low synoptic scale relationships, which have a direct influence in the CN obtained. We carry out a comparison of the proposed approach with a classical correlation-based CN construction method. We show that the proposed approach based on the SODCC algorithm and the KLD constructs CNs with an emergent behaviour according to underlying wind speed prediction data physics, unlike the correlation-based method that produces spurious and missing links. Furthermore, it is shown that the climate network construction method facilitates the evaluation of symmetry properties in the resulting complex networks.

scholarly journals D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S13) ◽  
Author(s):  
Xiang Chen ◽  
Min Li ◽  
Ruiqing Zheng ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Network ◽  
Construction Method ◽  
Data Driven ◽  
Regression Problem ◽  
Network Construction ◽  
Benchmark Datasets ◽  
Gene Regulatory ◽  
New Perspective

Abstract Background To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem. Results In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR. Conclusions We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

A fast multi-output RBF neural network construction method

2010 ◽  
Vol 73 (10-12) ◽  
pp. 2196-2202 ◽  
Author(s):  
Dajun Du ◽  
Kang Li ◽  
Minrui Fei
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Construction Method ◽  
Network Construction

A new perspective of link prediction in complex network for improving reliability

2020 ◽  
pp. 2150006
Author(s):  
Shuang Gu ◽  
Keping Li ◽  
Liu Yang
Keyword(s):  
Link Prediction ◽  
High Speed ◽  
Network Reliability ◽  
Network Evolution ◽  
Railway Network ◽  
Network Construction ◽  
Scale Free ◽  
Evaluation Indexes ◽  
Highway Network ◽  
New Perspective

Link prediction is an important issue for network evolution. For many real networks, future link prediction is the key to network development. Experience shows that improving reliability is an important trend of network evolution. Therefore, we consider it from a new perspective and propose a method for predicting new links of evolution networks. The proposed network reliability growth (NRG) model comprehensively considers the factors related to network structure, including the degree, neighbor nodes and distance. Our aim is to improve the reliability in link prediction. In experiments, we apply China high-speed railway network, China highway network and scale-free networks as examples. The results show that the proposed method has better prediction performance for different evaluation indexes. Compared with the other methods, such as CN, RA, PA, ACT, CT and NN, the proposed method has large growth rate and makes the reliability reach the maximum at first which save network construction resources, cost and improve efficiency. The proposed method tends to develop the network towards homogeneous network. In real networks, this structure with stronger stability is the goal of network construction. Therefore, our method is the best to improve network reliability quickly and effectively.

scholarly journals COGENT: evaluating the consistency of gene co-expression networks

2020 ◽  
Author(s):  
Lyuba V Bozhilova ◽  
Javier Pardo-Diaz ◽  
Gesine Reinert ◽  
Charlotte M Deane
Keyword(s):  
External Validation ◽  
Construction Method ◽  
Supplementary Information ◽  
Validation Data ◽  
Network Construction

Abstract Summary Gene co-expression networks can be constructed in multiple different ways, both in the use of different measures of co-expression, and in the thresholds applied to the calculated co-expression values, from any given dataset. It is often not clear which co-expression network construction method should be preferred. COGENT provides a set of tools designed to aid the choice of network construction method without the need for any external validation data. Availability and implementation https://github.com/lbozhilova/COGENT Supplementary information Supplementary information is available at Bioinformatics online.

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