Assortative Analysis of Bulk Trade Complex Network on Maritime Silk Road

Mou, Naixia; Fang, Yujie; Yang, Tengfei; Zhang, Lingxian

doi:10.1109/access.2020.3009970

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Assortativity quantifies the mixing pattern of complex networks, which measures whether the node is preferentially connected to a node with a similar scale [53]. In a directed network, we consider the correlation of four degree directions.…”

Section: Assortativitymentioning

confidence: 99%

Structural evolution of international crop trade networks

Zhang

Zhou

2022

Front. Phys.

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Food security is a critical issue closely linked to human being. With the increasing demand for food, international trade has become the main access to supplementing domestic food shortages, which not only alleviates local food shocks, but also exposes economies to global food crises. In this paper, we construct four temporal international crop trade networks (iCTNs) based on trade values of maize, rice, soybean and wheat, and describe the structural evolution of different iCTNs from 1993 to 2018. We find that the size of all the four iCTNs expanded from 1993 to 2018 with more participants and larger trade values. Our results show that the iCTNs not only become tighter according to the increasing in network density and clustering coefficient, but also get more similar. We also find that the iCTNs are not always disassortative, unlike the world cereal trade networks and other international commodity trade networks. The degree assortative coefficients depend on degree directions and crop types. The analysis about assortativity also indicates that economies with high out-degree tend to connect with economies with low in-degree and low out-degree. Additionally, we compare the structure of the four iCTNs to enhance our understanding of the international food trade system. Although the overall evolutionary patterns of different iCTNs are similar, some crops exhibit idiosyncratic trade patterns. It highlights the need to consider different crop networks’ idiosyncratic features while making food policies. Our findings about the dynamics of the iCTNs play an important role in understanding vulnerabilities in the global food system.

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Section: Assortativitymentioning

confidence: 99%

Structural evolution of international crop trade networks

Zhang

Zhou

2022

Front. Phys.

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“…A large number of valuable research results have been obtained based on the degree correlation properties of networks [10][11][12][13][14]. In this section, by describing the degree correlation of complex networks and combining the composition characteristics of conflict networks, they are classified into assortative, disassortative and neutral conflict networks.…”

Section: Degree Correlation Of the Networkmentioning

confidence: 99%

“…In the field of aviation, there are fewer studies based on network degree correlation, but there are a large number of studies in other fields that are worthy of reference: Kai Mao [10] used the degree correlation property to classify complex networks into three types and explored the stability and robustness of complex networks using variable gradient analysis; Wang et al [11] analyzed the variation of the basic regeneration number R-0 of SIR epidemics in degree correlation networks. mou et al [12] provided theoretical references for port planning and national bulk trade strategies by analyzing trade networks with different degree correlation characteristics; Lu et al [13] identified key nodes in complex networks by constructing similarity coefficients; Zhao Na et al [14] classified the dependent networks into heterogeneous, homogeneous and random dependent ways according to the degree value of nodes and then analyzed the network robustness. Few of the above studies have analyzed degree correlation networks with different strengths or weaknesses, and have not clarified the changes in robustness of networks with different degree matching types after being subjected to deliberate and random attacks.…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of Flight Conflict Networks Based on Degree Correlation

Wen

Lin

et al. 2022

IEEE Access

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Analyzing the composition and robustness of flight conflicts is beneficial to find the vulnerable sources of conflicts and dissipate flight conflicts, and effectively improve the safety and operational efficiency of air traffic. Therefore, this paper proposes a flight conflict network robustness evaluation method based on degree correlation. Firstly, a flight conflict network is constructed by using the velocity obstacle method, and it is classified into three types of assortative, disassortative and neutral conflict networks according to the degree correlation of the network. Several common robustness metrics in complex networks are explored, and a comprehensive robustness metric is proposed. The relationship between degree correlation and robustness of conflict networks under different node removal methods is analyzed by simulation experiments. The results show that the false alarm rate of the flight conflict network is decreased by 37.5% compared with the traditional state network. The degree correlation of the flight conflict network is closely related to the robustness. The assortative and disassortative conflict networks demonstrate stronger vulnerability with degree and betweenness removal, respectively, and the stronger the correlation the more obvious the vulnerability. The robustness of the neutral conflict network does not differ significantly under the two removal methods. Compared with random deletion, target removal can significantly decrease the network robustness.INDEX TERMS Flight conflict; air traffic; complex networks; degree correlation; robustness

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“…With the expansion of the fragmentation of production and global production networks, enterprises as crucial carriers of numerous elements such as talent, capital, and information are reshaping the new urban network development pattern [16]- [17]. Taylor is one of the earliest scholars who applied enterprise networks and interlocking network models to examine urban networks.…”

mentioning

confidence: 99%