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This study analyzed Korea's relations table through network analysis. In particular, among the centralities, eigenvector centrality, PageRank centrality and degree were used. The author studied which network characteristics affected the value-added rate.

A network analysis method was used.

It is the inward relationship that affects the value-added ratio of Korea's industries and the outward relationship has less influence. In particular, the inward relationship not only acts as a cost but also has an effect on the rate of added value recently.

Since the three years of 2010, 2015 and 2019 are the target, the data are somewhat insufficient to generalize.

As for the value-added ratio of an industry, input is more important than output (sales). Therefore, where the input is received is very important.

It is possible to increase the understanding of the determinants of the value-added rate of Korean industries.

(1) It was clarified which side is inward or outward in determining the industry in Korea. (2) The relationship between PageRank, eigenvector centrality and degree was analyzed in Korean cases. (3) Input is a cost and acts to increase added value.

The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level.

This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs.

The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network.

The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities.

By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

Recently, decoupling between the US and China has emerged as an important issue in global economics. We propose an analytical framework for trade decoupling analysis, borrowing the idea from the production function in non-competitive input-output tables. Using that methodology, we analyze the mobile phone trade network subject to various measures imposed by the US. A scenario analysis is performed to compare the extent of decoupling after a trade war with worst-cases. In bilateral trade, China’s share of total US imports fell significantly in 2019 compared to 2017. However, China’s indirect exports to the US increased during the same period. A similar pattern is observed in the global trade network visualized via multidimensional scaling (MDS). China’s out-degree centrality decreased slightly, while Vietnam’s role expanded. Actual figures for 2019 show a decreased out-degree centrality for Chinese final good exports, but a much higher one in the scenarios. Also, China’s indirect exports to the US have increased. But China does not appear to play a key role in the network as assumed in the scenario. Throughout the study, intermediate goods were treated homogeneously, and further studies considering the heterogeneity of input-output linkages are needed.

In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features.

This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network.

Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs.

The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy.

The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

This chapter investigates the effects of economic development, FDI, trade barriers, product characteristics, and air transport network connectivity on both air trade and air cargo demand. The analysis applies gravity model and estimates the air trade and air cargo demand models using seemingly unrelated regressions based on data for the air cargo markets between the United States and its top 61 trading partner countries during the 2004–2019 period. By developing and incorporating “investment distance” as a determining factor in the estimation of air trade, our study fills the gap in literature and sheds light on the importance of air cargo transport in enabling and facilitating the rapid growth of global value chains in recent decades. The results suggest that higher level of FDI between the US and its trading partner countries helps stimulate air trade. Moreover, we also develop several network centrality metrics and examine their relationship with regional air connectivity, which in turn has a positive impact on air cargo traffic. Further analysis using Granger causality tests provides strong evidence supporting the importance of air cargo services as an engine for economic growth and international trade in a dynamic global economic landscape.

Using overlapped portfolio data on public equity funds in Korea, the authors construct several types of fund-stock weighted bipartite networks and measure fund network centrality. The authors also examine the relationship between network centrality and fund investment performance. The authors' results are three-fold. First, the authors find that the fund centrality of the network in which funds and stocks are connected based on the most active investing behavior positively affects the fund performance. Second, the funds with a high centrality level based on the same network generate higher returns by holding stocks with high value uncertainty. Third, the authors find that fund centrality is not associated with herd behavior. Based on these results, the authors argue that fund centrality is a proxy of information advantage and skill of fund managers. The authors' paper shows that network analysis could be a new way to identify funds with better performance and measure the skill and information advantage to construct an optimal portfolio.

This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances.

This study aims to review the literature on graph-theoretic mining methods for node importance in both static and dynamic world city networks, which is correspondingly categorised by graph-theoretic node importance mining on network topologies and transmission mechanisms.

The authors overview the graph-theoretic indicators of node importance: centrality and power. Then, the methods of graph-theoretic node importance mining on network topologies are assessed with node relevance, centrality- and power-based measurements, heterogeneous fusion and other miscellaneous approaches. The latest progress in transmission mechanisms is also reviewed in this study involving network evolution, node immunisation and robustness in dynamics. Finally, the findings are analysed and future directions in this field are suggested.

The method development of node importance mining is driven by complex application-based problems within a transmission mechanism. Fusion measurements, based on centrality and power, are extended by other graph mining techniques in which power has a significant role. In conclusion, the trends of node importance mining focus on power-embedded fusion measurements in the transmission mechanism-based complex applications.

This is the first systematic literature review of node importance from the view of graph-theoretic mining.

This study aims to examine the impact of a chief executive officer (CEO) social network centrality on corporate social responsibility (CSR) performance.

This study carries out a multivariate linear regression analysis on a panel data sample of 11,507 firm-year observations (representing 1,386 unique US firms) from 2004 to 2014.

This paper finds a significant negative relation between CEO network centrality and irresponsible CSR performance (measured as CSR concerns). The findings suggest that better-connected CEOs can better mitigate CSR concerns or weaknesses, leading to improved overall CSR performance of a firm.

This is the first study that directly examines the empirical link between CEO centrality and CSR performance.

Qualitative methods are not suitable to process high volumes of policy texts for exploring policy evolution. Therefore, it is hard to use qualitative methods to systematically analyze the characteristics of complex policy networks. So the authors propose a bibliometric research study for exploring policy evolution from time–agency–theme perspectives to excavate the rules and existing problems of China's medical informatization policy and to provide suggestions for formulating and improving the future medical informatization policies.

Initially, 615 valid samples are obtained by retrieving related China's medical informatization policy documents, and the joint policy-making agency network and the co-occurrence network models of medical informatization policies are defined, and then the authors research China's medical informatization policies from single-dimension and multi-dimension view.

The analysis results reveal that China's medical informatization policy process can be divided into four stages; the policy-making agencies are divided into four subgroups by community detection analysis according to the fast unfolding algorithm; the core policy theme keywords are identified based on the eigenvector centrality of the nodes in those networks; the focuses of theme terms are varied in different stages and the correlations between agencies and themes are gradually decentralized.

These findings provide experience and evidence on leveraging informatics in the medical and healthcare field of China. Also, they can help scholars and practitioners better understand the current status and future directions of medical and healthcare informatics development in China and provide a reference to formulate and improve China's future medical informatization policies.

This study proposes a quantitative bibliometric-based research framework to describe transitions and trends of China's medical informatization policy.