celegans_multiplex — C. elegans multiplex connectome (2006)

Description

A multiplex network of the neurons of the hermaphrodite nematode Caenorhabditis elegans, distributed by M. De Domenico and based on the wiring diagram compiled by Chen, Hall and Chklovskii (2006) from the serial-section electron micrographs of White et al. (1986). Nodes are the 279 neurons, identified by their names, and an undirected edge in a layer connects two neurons joined by a synapse of the corresponding type. There are three layers:

ElectrJ (517 edges): electrical junctions (gap junctions);
MonoSyn (888 edges): monadic chemical synapses, with a single postsynaptic partner;
PolySyn (1,703 edges): polyadic chemical synapses, with several postsynaptic partners.

The README of the data set describes the network as undirected and unweighted. Although it says that only one direction of each edge is specified, most pairs of neurons are listed in both orientations within a layer (all of them in ElectrJ, and about 90% in the two chemical layers), so the direction does not distinguish pre- from postsynaptic neurons; such pairs are included here only once, which reduces the 5,863 listed connections to 3,108 edges. The ElectrJ layer contains three self-loops (RIBL, RIBR and VA08). Neither the number of synapses nor their direction is available.

The same neurons, with the direction and the number of synapses of each connection, are in the entry celegans_varshney, which is based on the updated and revised reconstruction of Varshney et al. (2011), in which chemical synapses are not separated into monadic and polyadic ones.

The network has the following properties:

Nodes: name and nodeLabel, the neuron name (e.g. ADAL).
Edges: layer, the synapse type (1: electrical junction, 2: monadic chemical synapse, 3: polyadic chemical synapse; the names are stored in the graph property layer_key); weight, which is always 1.

Tags

Biological Connectome Multilayer Unweighted

Citation

B. L. Chen, D. H. Hall, D. B. Chklovskii, "Wiring optimization can relate neuronal structure and function." Proceedings of the National Academy of Sciences 103(12), 4723-4728 (2006) 10.1073/pnas.0506806103 Sci-Hub
M. De Domenico, M. A. Porter, A. Arenas, "MuxViz: a tool for multilayer analysis and visualization of networks." Journal of Complex Networks 3(2), 159-176 (2015) 10.1093/comnet/cnu038 Sci-Hub

Upstream URL OK

https://manliodedomenico.com/data.php

Upstream license

Open Data Commons Open Database License (ODbL), https://opendatacommons.org/licenses/odbl/

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_multiplex	279	3,108	22.28	18.46	24.37	5.95	-0.09	0.22	5	1.00	Undirected	Unipartite	name nodeLabel	weight layer	10 KiB	18 KiB	18 KiB	15 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.