Identifying power elites—k-cores in heterogeneous affiliation networks

Larsen, Anton Grau; Ellersgaard, Christoph

doi:10.1016/j.socnet.2017.03.009

Cited by 47 publications

(36 citation statements)

References 54 publications

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“…A number of different techniques can be used for identifying core-periphery structures within networks, 62 however, we opt for a method that has recently been deployed to the study of economic elites, as this is closer to the other kinds of networks that have been used for other core-periphery analyses, such as the world wide web, transportation networks, or networks of proteins or cells, for example. 63 We use the technique of Larsen and Ellersgaard (2017) to define a core based on minimal distance paths from each node to each other node, generating a "coreness" score. 64 We take those organizations as being one standard deviation above the mean of this score of "coreness" to indicate being in the core, which we record as a 1, and 0 for not being in the core and thus in the periphery.…”

Section: Key Explanatory Variablementioning

confidence: 99%

Network effects in the formation of the financial industry's regulatory preferences in the European Union

Chalmers¹,

Young²

2020

Bus. Polit.

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This paper examines the determinants of financial industry actors’ regulatory preferences—examining why some financial industry actors prefer less stringent financial regulations while others prefer more stringent regulations. The determination of preferences, we argue, can be understood as mutually dependent. How an organization is connected to other organisations through network ties may help to explain its regulatory preferences. Our empirical point of focus is financial industry lobbying in the context of the European Union (EU). Using data from nearly nine hundred lobbying letters related to legislation on banking, insurance, and securities regulation, we map out a “socialization network” that models connections between financial industry firms, their associations, as well as a broad range of other organisations and actors that are auxiliary to this community of organizations. Using these data we find evidence that organizations’ preferences are informed by their location within this socialization network. Controlling for a range of other plausible factors, we find that 1) those connected via common associational ties, 2) those closer to one another in the network and 3) those more “embedded” in this network are all less likely to diverge in terms of their preferences from one another.

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Section: Key Explanatory Variablementioning

confidence: 99%

Network effects in the formation of the financial industry's regulatory preferences in the European Union

Chalmers¹,

Young²

2020

Bus. Polit.

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“…Common for these three approaches is a relatively high vulnerability to more or less ad hoc decisions (Larsen and Ellersgaard 2017). Even the demarcation of a somewhat straightforward group, e.g.…”

Section: Defining the Population An All But Trivial Questionmentioning

confidence: 99%

“…These questions require a reflexive movement back and forth between data and hypotheses, similar to a hermeneutic circle in which qualitative knowledge of the field informs and is informed by data (Bourdieu 2005, 99). We propose here that this process can be further guided and substantiated by systematic use of relational methods (Larsen and Ellersgaard 2017; see also Knoke 1993) using patterns of formal interaction to identify key players at the core of elite networks.…”

Section: Defining the Population An All But Trivial Questionmentioning

confidence: 99%

“…Affiliation data can then be used to explore and create variables covering which sectors or types of affiliations individuals are engaging in. In turn, for each individual, the centrality (Freeman 1979), positions in core or peripheral groups in the network (Larsen and Ellersgaard 2017) or in certain clusters or communities (see e.g. Heemskerk et al 2013;Palla et al 2005), can be calculated.…”

Section: Social Network Datamentioning

confidence: 99%

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The Craft of Elite Prosopography

Lunding

Ellersgaard

Larsen

2020

Researching Elites and Power

Self Cite

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“…As stated in [31], it is still not well understood what the evaluation models for social influence studies are and the research field is still on its preliminary stages. The authors' goal in [32] is to identify top-k influential (or powerful) nodes in a society as a whole. They propose a method for including nodes in the network and gathering data from various data sets, such as top foundations and corporations' executive boards, all in Denmark.…”

Section: Influence Analysis Background From Social Network Analysis Rmentioning

confidence: 99%

Metrics for network power based on Castells’ Network Theory of Power: a case study on Brazilian elections

Verona

Oliveira

Hisse

et al. 2018

J Internet Serv Appl

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The identification of power groups that act within the political structure is a tool for citizens and also a challenging research topic as an unstable and complex force of interests system. This paper presents metrics based on sociologist Manuel Castells' Network Theory of Power that reflect key factors for evaluating power: the imbalance between relationships; the ability to program the rules and protocols of the network; and the ability to serve as a switcher between two or more networks. A case study was developed using a network built on data from Brazilian Elections about electoral donations since 2002. The application of the metrics enabled to highlight some of the corporate and party interests dominant in the Brazilian context. The proposed metrics reflect the main contribution of this work: an approximation between sociology theory and topological analysis of a network. The use of domain knowledge combined with bottom-up strategies can leverage the comprehension of power and influence in political networks.

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Identifying power elites—k-cores in heterogeneous affiliation networks

Cited by 47 publications

References 54 publications

Network effects in the formation of the financial industry's regulatory preferences in the European Union

Network effects in the formation of the financial industry's regulatory preferences in the European Union

The Craft of Elite Prosopography

Metrics for network power based on Castells’ Network Theory of Power: a case study on Brazilian elections

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