eva_ownership — EVA corporate ownership network (2002)

Description

Ownership relations among companies in the telecommunications and media industries, from the EVA (Extraction, Visualization and Analysis of corporate inter-relationships) project of the Denali group at the University of California, Berkeley. The relations were extracted automatically, using probabilistic information retrieval and extraction techniques, from heterogeneous sources of online text, including the corporate annual reports (10-K) filed with the U.S. Securities and Exchange Commission. A directed edge points from a company to a company it owns (a parent-subsidiary relation). Ownership relations are not available for all companies, so many companies have no edges. The data were converted into Pajek format by V. Batagelj (2004). The data were distributed in Pajek format on the Pajek datasets page of V. Batagelj and A. Mrvar, from which this version was obtained. The Pajek file declares 8497 vertices, but only 8325 identifiers are used (8322 with a company name, and 3 unnamed companies with edges); the unused identifiers are not included.

The network has the following properties:

Nodes: name, the name of the company ("?" for 100 companies whose name is unknown, and empty for the 3 unnamed ones).

Tags

Economic Commerce Unweighted Metadata

Citation

K. Norlen, G. Lucas, M. Gebbie, J. Chuang, "EVA: Extraction, Visualization and Analysis of the Telecommunications and Media Ownership Network." Proceedings of the International Telecommunications Society 14th Biennial Conference, Seoul (2002) web.archive.org

Upstream URL OK

https://web.archive.org/web/20040331022415/http://vlado.fmf.uni-lj.si/pub/networks/data/econ/Eva/Eva.htm

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
eva_ownership	8,325	6,726	0.81	9.03	5.28	1477.84	-0.18	0.00	18	0.54	Directed	Unipartite	name		208 KiB	257 KiB	254 KiB	222 KiB

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.