celegans_metabolic — Metabolic network (C. elegans)

Description

The metabolic network of the nematode Caenorhabditis elegans, as used by J. Duch and A. Arenas to test their community detection method based on extremal optimization. Nodes are metabolites (substrates), and an undirected edge connects two metabolites that participate in the same metabolic reaction. The data are derived from the metabolic network reconstructions of H. Jeong et al., Nature 407, 651 (2000), which are based on the WIT database. Nodes are identified only by anonymous labels (v1 to v453), so the identities of the metabolites are not available. The network contains parallel edges (pairs of metabolites connected by more than one edge) and a few self-loops, as present in the source file.

The network has the following properties:

Nodes: name and id, the node label (v1 to v453); x, y and z, the node coordinates in the Pajek source file (without meaning).
Edges: weight, which is always 1.

Tags

Biological Metabolic Unweighted

Citation

J. Duch and A. Arenas, "Community identification using Extremal Optimization." Physical Review E 72, 027104 (2005). 10.1103/physreve.72.027104 Sci-Hub

Upstream URL RuntimeError: a web page instead of celegans_metabolic.zip (e.g. an error page of the server)

https://deim.urv.cat/~alexandre.arenas/data/welcome.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
celegans_metabolic	453	4,596	20.29	52.23	23.97	21.93	-0.06	0.35	7	1.00	Undirected	Unipartite	id name x y z	weight	12 KiB	18 KiB	25 KiB	18 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.