Discovering hidden information of gene ontology based on complex networks analysis

To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empirical studies of the gene ontology with various perspectives, this paper shows that the whole gene ontology displays the same topological features as complex networks including ＂small world＂ and ＂scale-free＂,while some sub-ontologies have the ＂scale-free＂ property but no ＂small world＂ effect.The potential important terms in an ontology are discovered by some famous complex network centralization methods.An evaluation method based on information retrieval in MEDLINE is designed to measure the effectiveness of the discovered important terms.According to the relevant literature of the gene ontology terms,the suitability of these centralization methods for ontology important concepts discovering is quantitatively evaluated.The experimental results indicate that the betweenness centrality is the most appropriate method among all the evaluated centralization measures.

Core acupoints of acupuncture for hyperplasia of mammary gland: a network analysis

Backgroud:Clinical studies on acupuncture treatment of hyperplasia of mammary gland(HMG)have proved its effectiveness,but most studies have paid little attention to acupoints prescription and acupoint compatibility.The clinical prescription is not identical,the curative effect also has the difference.Therefore,through data mining and network analysis,this study explored the core acupoints and the compatibility law of acupoints in acupuncture treatment of HMG.Methods:To search and select qualified literature according to inclusion and exclusion criteria for relevant clinical research literature on acupuncture treatment of HMG in CNKI,VIP database,WanFang database and PubMed,etc.Then extract relevant information and establish a database.Using the method of statistical and complex network analysis,this paper studies the core acupoints and the law of acupoint compatibility.Results:A total of 104 Chinese literatures and 0 English literatures were included and 106 acupuncture prescriptions were extracted.The core acupoints in the treatment of HMG are Danzhong(CV 17),Wuyi(ST 15),Zusanli(ST 36),Jianjing(GB 21).Danzhong(CV 17)and Zusanli(ST 36),Danzhong(CV 17)and Wuyi(ST 15),Jianjing(GB 21)and Tianzong(SI 11),Jianjing(GB 21)and Wuyi(ST 15)have the highest correlation degree.The method of acupoint matching mainly consists of local-remote acupoints,upper-lower acupoints and front-rear acupoints.Conclusion:The results of a network analysis substantially accord with the general rules of acupuncture theories in traditional Chinese medicine,able to reflect the points-selection principles and features in acupuncture treatment of HMG and provide evidence for the acupoints selection in the treatment of HMG in acupuncture clinic.

Evolution of IPv6 Internet topology with unusual sudden changes

The evolution of Internet topology is not always smooth but sometimes with unusual sudden changes. Consequently, identifying patterns of unusual topology evolution is critical for Internet topology modeling and simulation. We analyze IPv6 Internet topology evolution in IP-level graph to demonstrate how it changes in uncommon ways to restructure the Internet. After evaluating the changes of average degree, average path length, and some other metrics over time, we find that in the case of a large-scale growing the Internet becomes more robust; whereas in a top–bottom connection enhancement the Internet maintains its efficiency with links largely decreased.

Progress of Studies on Traditional Chinese Medicine Based on Complex Network Analysis

Traditional Chinese medicine(TCM) is a distinct medical system that deals with the life-health-disease-environment relationship using holistic, dynamic, and dialectical thinking. However, reductionism has often restricted the conventional studies on TCM, and these studies did not investigate the central concepts of TCM theory about the multiple relationships among life, health, disease, and environment. Complex network analysis describes a wide variety of complex systems in the real world, and it has the potential to bridge the gap between TCM and modern science owing to the holism of TCM theory. This article summarizes the current research involving TCM network analysis and highlights the computational tools and analysis methods involved in this research. Finally, to inspire a new approach, the article discussed the potential problems underlying the application of TCM network analysis.

Emergency road network structure and planning optimization in mountainous regions in Southwest China under earthquake scenarios

Emergency road networks(ERNs),an important part of local disaster prevention systems,can provide security to residents and their property.Exploring the ERNs structure is of great significance in terms of promoting disaster prevention and establishing road safety in dangerous mountainous areas.This study considered the ERNs of the Kangding section of the Dadu River Basin as the area for a case study.Complex Network Analysis was used to examine the relationship between the four characteristic indicators of mountain roads and the degree of earthquake impacts under the Lushan,Wenchuan,and Kangding Earthquake scenarios.Based on the analysis results,the southwest mountain road network was evaluated;then,computer simulations were used to evaluate the structural changes in the road network after index changes.The network was optimized,and the corresponding emergency avoidance network was proposed to provide a reference for the establishment of the mountainous ERN.The results show that the overall completeness of the mountainous ERNs in Southwest China is poor and prone to traffic accidents.Moreover,the local stability is poor,and the network is susceptible to natural hazards.The overall structure of the road network is balanced,but that of certain road sections is not.Road sections with different attributes present a“gathering-scattering”spatial distribution,i.e,some sections are clustered together while others are far apart.Accordingly,a planning optimization strategy is proposed to better understand the complexity and systematic nature of the mountainous ERN as a whole and to provide a reference for disaster prevention and mitigation planning in mountainous regions in Southwest China.

Deductive way of reasoning about the internet AS level topology

Our current understanding about the AS level topology of the Internet is based on measurements and inductive-type models which set up rules describing the behavior （node and edge dynamics） of the individual ASes and generalize the consequences of these individual actions for the complete AS ecosystem using induction. In this paper we suggest a third, deductive approach in which we have premises for the whole AS system and the consequences of these premises are determined through deductive reasoning. We show that such a deductive approach can give complementary insights into the topological properties of the AS graph. While inductive models can mostly reflect high level statistics （e.g., degree distribution, clustering, diameter）, deductive reasoning can identify omnipresent subgraphs and peering likelihood. We also propose a model, called YEAS, incorporating our deductive analytical findings that produces topologies contain both traditional and novel metrics for the AS level Internet.

CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms

Crude oil price prediction is a challenging task in oil producing countries.Its price is among the most complex and tough to model because fluctuations of price of crude oil are highly irregular,nonlinear and varies dynamically with high uncertainty.This paper proposed a hybrid model for crude oil price prediction that uses the complex network analysis and long short-term memory(LSTM)of the deep learning algorithms.The complex network analysis tool called the visibility graph is used to map the dataset on a network and K-core centrality was employed to extract the non-linearity features of crude oil and reconstruct the dataset.The complex network analysis is carried out in order to preprocess the original data to extract the non-linearity features and to reconstruct the data.Thereafter,LSTM was employed to model the reconstructed data.To verify the result,we compared the empirical results with other research in the literature.The experiments show that the proposed model has higher accuracy,and is more robust and reliable.

Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry

Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.

Evolution of the rare earth trade network:A perspective of dependency and competition

As a global strategic reserve resource,rare earth has been widely used in important industries,such as military equipment and biomedicine.However,existing analyses based solely on the total volume of rare earth trade fail to uncover the underlying competition and dependency dynamics.To address this gap,this paper employs the principles of trade preference and import similarity to construct dependency and competition networks.Complex network analysis is then employed to study the evolution of the global rare earth trade network from 2002 to 2018.The main conclusions are as follows.The global rare earth trade follows the Pareto principle,and the trade network shows a scale-free distribution.China has emerged as the world’s largest importer and exporter of rare earth since 2017.In the dependency network,China has become the most dependent country since 2006.The result of community division shows that China has separated from the American community and formed new communities with the Association of Southeast Asian Nations(ASEAN)countries.The United States of America has formed a super-strong community with European and Asian countries.In the competition network,the distribution of competition intensity follows a scale-free distribution.Most countries face low-intensity competition,but there are numerous competing countries.The competition related to China has increased significantly.Lastly,the competition source for the United States of America has shifted from Mexico to China,resulting in China,the USA,and Japan becoming the core participants in the competition network.