corporate_directors — Global corporate directors (2016)

Description

A bipartite network of company directors and the boards on which they sit, for about 36,000 companies traded on stock exchanges in 54 countries worldwide, compiled by A. Evtushenko and M. T. Gastner to study the representation of women in a global network of directors. The data were collected (c. September 2016) from the website of the Financial Times, which obtains its information from Thomson Reuters. For each company, the name, sector, industry, country and number of employees were recorded, and for each director the name, gender and age, when available. Directors appearing in different companies were identified as the same person if their name, gender and age all matched. No distinction is made between executive and non-executive directors. An undirected edge connects a director to each company on whose board they sit.

The network has the following properties:

(Node) is_company: whether the node is a company (otherwise it is a director).
(Node) age (directors): the age in years.
(Node) gender (directors): Male or Female.
(Node) country, sector (companies): the country and sector of the company.
(Node) industry (companies): the industry (a subsector).
(Node) employeesnum (companies): the number of employees.

Where the information is unavailable or not applicable (e.g. company properties of directors), the text properties have the value NA and the numerical properties (age and employeesnum) the value NaN. Names are not included.

Tags

Economic Governance Unweighted Metadata

Added

2020-08-11

Citation

A. Evtushenko and M. T. Gastner, "Beyond Fortune 500: Women in a Global Network of Directors." In H. Cherifi et al. (Eds.), Complex Networks and Their Applications VIII, Volume 1, pp. 586-598 (2020) arXiv:1910.07441

Upstream URL OK

https://doi.org/10.5281/zenodo.3553442

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
corporate_directors	356,638	377,060	2.11	3.60	13.48	2771.10	-0.48	0.00	48	0.74	Undirected	Bipartite	age country employeesnum gender industry sector type		6.1 MiB	9.0 MiB	8.6 MiB	7.6 MiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.