How the network properties of shareholders vary with investor type and country

Yao, Qing; Evans, T. S.; Christensen, Kim

doi:10.1371/journal.pone.0220965

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…1. Similar behaviours are observed for shareholder networks for Netherlands and Turkey [3]. The universal behaviours of the complex networks may emerge due to some common mechanism.…”

Section: Introductionsupporting

confidence: 66%

“…To account for local interactions, we propose a different model, which is based on the random walks rewiring of networks consisting of different types of nodes. We show that the model reproduces the main statistical characteristics of real-world systems: the emergence of scaling of communities and percolation properties, the distribution of average shortest path and the dependence of above properties on the types of nodes in the data, especially the results in the work of Shareholder networks [3].…”

Section: Introductionmentioning

confidence: 74%

“…In the example of shareholder networks [3], we first created a directed graph with links representing shareholders investing in companies. Then we analysed the shareholder networks where links represent the common companies that shareholders hold in common.…”

Section: The Model 21 Random Walk Rewiring On Directed Networkmentioning

confidence: 99%

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Emergence of community structures through biased random walks rewiring

Yao¹,

Chen²,

Evans³

et al. 2022

Preprint

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Community structures have been identified in various complex real-world networks, for example, communication, information, internet and shareholder networks. The scaling of community size distribution indicates the heterogeneity in topological structures of the network. The current network generating or growing models can reproduce some properties, including degree distributions, large clustering coefficients and communities. However, the scaling behaviour of the community size lacks investigation, especially from the perspectives of local interactions. Based on the assumption that heterogeneous nodes behave differently and result in different topological positions of the networks, we propose a model of designed random walks in directed networks to explain the features in the observed networks. The model highlights that two different dynamics can mimic the local interactions, and a hidden layer is essential when reproducing the characteristics of real complex networks. The key features the model can explain include community size distribution, degree distribution, percolation properties, distribution of average path length and dependence of the above properties on the labels of nodes in the data.

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“…1. Similar behaviours are observed for shareholder networks for Netherlands and Turkey [3]. The universal behaviours of the complex networks may emerge due to some common mechanism.…”

Section: Introductionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 74%

See 1 more Smart Citation

Emergence of community structures through biased random walks rewiring

Yao¹,

Chen²,

Evans³

et al. 2022

Preprint

Self Cite

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show abstract

“…If we assume that graph evolution follows this pairwise mechanism, we can estimate values p(s) and q(s) for the parameters p and q respectively by looking at how links changed over one time step, i.e., from the edge set E (s − 1) in G (s − 1) to edge set E (s) of G (s) . Formally we have that This gives us our pairwise model prediction for the triplet transition matrix T (pw) (s) , where we substitute p(s) from (6) and q(s) from (5) for p and q in T (pw) 51 . The nodes are shareholders in Turkish companies and two shareholders are linked if they both hold shares in the same company in one year.…”

Section: Evidence For Higher Order Interactionsmentioning

confidence: 99%

Higher-order temporal network effects through triplet evolution

Yao

Chen

Christensen

et al. 2021

Sci Rep

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We study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

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“…Investor decisions on which the country chooses for investment depend on a number of different factors ranging from the price of the labour force, availability of the requisite competences, the country's geopolitical location, the tax system, intensity of market competition, and political stability in the country [59][60][61] to lobbying [62,63], managerial discretion [64], clusters, and networking [65]. All these factors are in line with the investment attractiveness concept.…”

Section: Investment Attractiveness: a Smartness Approachmentioning

confidence: 99%

Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?

et al. 2019

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A rich volume of literature has analysed country investment attractiveness in a wide range of contexts. The research has mostly focused on traditional economic concepts—economic, social, managerial, governmental, and geopolitical determinants—with a lack of focus on the smartness approach. Smartness is a social construct, which means that it has no objective presence but is “defined into existence”. It cannot be touched or measured based on uniform criteria but, rather, on the ones that are collectively agreed upon and stem from the nature of definition. Key determinants of smartness learning—intelligence, agility, networking, digital, sustainability, innovativeness and knowledgeability—serve as a platform for the deeper analysis of the research problem. In this article, we assessed country investment attractiveness through the economic subjects’ competences and environment empowering them to attract and maintain investments in the country. The country investment attractiveness was assessed by artificial intelligence (in particular, neural networks), which has found widespread application in the sciences and engineering but has remained rather limited in economics and confined to specific areas like counties’ investment attractiveness. The empirical research relies on the case of assessing investment attractiveness of 29 European countries by the use of 58 indicators and 31,958 observations of annual data of the 2000–2018 time period. The advantages and limitations of the use of artificial intelligence in assessing countries’ investment attractiveness proved the need for soft competences for work with artificial intelligence and decision-making based on the information gathered by such research. The creativity, intelligence, agility, networking, sustainability, social responsibility, innovativeness, digitality, learning, curiosity and being knowledge-driven are the competences that, together, are needed in all stages of economic analysis.

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How the network properties of shareholders vary with investor type and country

Cited by 6 publications

References 30 publications

Emergence of community structures through biased random walks rewiring

Emergence of community structures through biased random walks rewiring

Higher-order temporal network effects through triplet evolution

Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?

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