world_trade_wf — International trade among 24 countries (Wasserman-Faust)

Description

Trade and diplomatic relations among 24 countries, selected by S. Wasserman and K. Faust (1994) for their social network analysis textbook from a list of 63 countries studied by D. A. Smith and D. R. White (1992), as a representative sample spanning the globe physically, economically and politically. The trade data refer to the period studied by Smith and White (1965-1980). The data were distributed by L. C. Freeman on his network data page at the University of California, Irvine (no longer online), from which this version was obtained via the Internet Archive.

The network has five directed layers (relations), in which an edge (i, j) indicates exports (or diplomats sent) from country i to country j:

MANUFACTURED_GOODS
FOODS: food and live animals.
CRUDE_MATERIALS: crude materials, excluding food.
MINERALS: minerals and fuels.
DIPLOMATIC_EXCHANGE

The network has the following properties:

Nodes: name, the name of the country; pop_growth, the average population growth between 1970 and 1981; gnp_growth, the average growth of the GNP per capita in the same period; schools, the secondary school enrollment ratio in 1981; energy, the energy consumption in 1981 (in kilo coal equivalents per capita). Missing values are NaN.
Edges: layer, the relation.

Tags

Economic Trade Multilayer Unweighted Metadata

Citation

D. A. Smith, D. R. White, "Structure and dynamics of the global economy: Network analysis of international trade 1965-1980." Social Forces 70(4), 857-893 (1992) 10.2307/2580193 Sci-Hub
S. Wasserman, K. Faust, "Social Network Analysis: Methods and Applications." Cambridge University Press (1994) 10.1017/CBO9780511815478 Sci-Hub

Upstream URL OK

https://web.archive.org/web/20210115130726/http://moreno.ss.uci.edu/data.html#trade

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
world_trade_wf	24	1,428	59.50	45.34	19.14	0.50	-0.27	0.87	2	1.00	Directed	Unipartite	name pop_growth gnp_growth schools energy	layer	3 KiB	6 KiB	6 KiB	6 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.