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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.

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.

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 aims to analyze how gender, research experience and geography are related to the researchers’ importance in the co-authorship network on management and accounting in Brazil.

A social network based on the co-authorship relationships in the papers published in leading Brazilian journals was examined using a logit model to estimate the probability of occupying prevailing positions.

The findings showed a network with a high level of fragmentation and a scarcity of authors serving as gatekeepers. Based on the number of directed links and collaboration with influential and well-connected authors, men were more likely to occupy central positions than women. Authors with higher academic degrees tended to establish more links but were more likely to distance from other authors. In terms of geography, authors from more- and less-favored regions may report similar propensity to occupy central positions.

Decision-makers should consider the importance of strengthening collaboration between different research groups and encourage female participation in broader collaborative networks. Research evaluation bodies should strengthen incentives regarding interinstitutional partnerships.

Studies on collaborative networks in management and accounting sciences are less common and generally focus on describing the networks. This paper combines social network analysis and econometric procedures to analyze the relationship between demographic and geographical aspects, and distinct network centrality indexes.

This paper aims to provide information about the structure of collaborative work among Argentinian economics. The study provides specific applied research of social network analysis focus on this profession in this specific country.

The contribution opted for applying social network analysis tools to papers presented in a congress and published in its proceedings. The authors focus in detecting main actors, groups of co-authorship, professionals acting as bridges between groups and differences between genders.

The paper provides empirical insights about how co-authorship has evolved between Argentine economists. The authors find that structural properties of the network, main actors, both male and female, main universities or center that affiliates them, a gender gap that might be closing out.

The paper focuses on the network for the period 1964-2014 without a more detailed dynamic. It also does not explain main topics worked by the authors.

The work provides knowledge about how groups are created in Economics in Argentina, how cooperation has evolved and what has been the role of women in this development. It also shows how different departments and entities collaborate with diverse success in the creation of new knowledge in Economics in Argentina.

The paper works with data from a source of information non-previously studied and contributes in explaining a particular type of collaborative work in a profession in Argentina.

The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics.

A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model.

The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA) and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods.

This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

The main purpose of this research paper is to generate a holistic bibliometric study of the tourism industry and COVID-19 fields, to further investigate the current interests and trends emerging from scientific collaboration and thematic analysis and to identify research gaps that indicate future research directions.

This study conducts several analyses, which include the co-authorship and social network analysis, co-citation and keyword co-occurrence knowledge structures. The authors generate a knowledge map of the leading articles and link them with previous literature to elucidate the debates and consensus in research on COVID-19 and tourism.

Research interests concentrate in the USA, China, Europe and the Oceania areas, so more cross-continental collaborations are expected among them and with other regions. Popular topics are tourism sustainable transformation, crisis management and multidisciplinary fields like tourism, hospitality, information technology and environmental sciences. This paper also identifies underexplored topics for future investigation on the social, environmental, cultural and governance dimensions of sustainable tourism.

This paper contributes to guiding tourism researchers in identifying and finding publication references and future collaborations. Moreover, the investigation of knowledge structures could be beneficial for scholars hoping to broaden the current understanding of this field and discover potential for future tourism research, especially in the global pandemic and other severe health crises.

This study enriches the existing literature in the fields of tourism and the pandemic and highlights current interests and research trends exploring scientific collaboration, thematic analysis and knowledge mapping.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

The purpose of this study is to examine the extent to which dynamic network analysis (DNA), text mining and natural language processing (NLP) are helpful research tools in identifying the key actors in a complex international crisis. The study uses these tools to identify the key actors in the Syrian crisis as a case study to validate the proposed algorithm.

To achieve its main purpose, the study uses a collection of three methodologies, namely, DNA, text mining and NLP.

The results of the analysis show four key actors in the Syrian crisis, namely, Russia, the USA, Turkey and China. The results also reveal changes in their powerful positions from 2012 to 2016, which matches the changes that occurred in the real world. The matching between the findings of the proposed algorithm and the real world events that happened in Syria validate our proposed algorithm and proves that the algorithm can be used in identifying the key actors in complex international crises.

The importance of the study lies in two main points. It proposes a new algorithm that mixes NLP, network extraction from textual unstructured data and DNA to understand and monitor changes occurring in a complex international crisis. It applies the proposed algorithm on the Syrian crisis as a case study to identify the key actors and hence validate the proposed algorithm.