foldoc — FOLDOC entries (2002)

Description

A network of cross-references among the terms of the Free On-line Dictionary of Computing (FOLDOC), a searchable dictionary of acronyms, jargon, programming languages, tools, architecture, operating systems, networking, theory, standards, companies, products and history, and in fact anything to do with computing, edited by Denis Howe since 1985 with contributions from hundreds of people. The network was extracted from the dictionary and cleaned by Vladimir Batagelj and Andrej Mrvar in February/June 2002, and distributed in their Pajek data repository.

Nodes are dictionary terms, and a directed edge $(X, Y)$ exists if the term $Y$ is used in the dictionary to describe the meaning of the term $X$. The Pajek repository page describes a network with 13,380 nodes and 120,700 edges, while the distributed file contains 13,356 nodes and 120,238 edges.

The network has the following properties:

(Node) name: the dictionary term.
(Edge) weight: the multiplicity of the edge (as given in the source).

Tags

Informational Web graph Weighted Metadata

Added

2020-08-11

Citation

V. Batagelj, A. Mrvar, M. Zaveršnik, "Network analysis of texts." Proceedings of the 5th International Multi-Conference Information Society - Language Technologies, 143-148, Ljubljana (2002) www.researchgate.net
D. Howe (Ed.), "FOLDOC: The Free On-line Dictionary of Computing" (2002) foldoc.org

Upstream URL OK

https://web.archive.org/web/20040331022112/http://vlado.fmf.uni-lj.si/pub/networks/data/dic/foldoc/foldoc.htm

Upstream license

GNU Free Documentation License, Version 1.1 or later, https://www.gnu.org/licenses/fdl-1.3.html

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
foldoc	13,356	120,238	9.00	16.01	32.39	13.39	0.00	0.16	8	1.00	Directed	Unipartite		weight	335 KiB	679 KiB	632 KiB	531 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.