Caenorhabditis elegans

This is the metabolic network of the roundworm Caenorhabditis elegans. Nodes are metabolites (e.g., proteins), and edges are interactions between them. Since a metabolite can iteract with itself, the network contains loops. The interactions are undirected. There may be multiple interactions between any two metabolites.


Internal namearenas-meta
NameCaenorhabditis elegans
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Metabolic network
Node meaningMetabolite
Edge meaningInteraction
Network formatUnipartite, undirected
Edge typeUnweighted, multiple edges
LoopsContains loops


Size n =453
Volume m =4,596
Unique edge count m̿ =2,040
Loop count l =22
Wedge count s =79,173
Claw count z =3,352,172
Cross count x =153,983,040
Triangle count t =3,284
Square count q =50,289
4-Tour count T4 =723,054
Maximum degree dmax =639
Average degree d =20.291 4
Fill p =0.019 838 4
Average edge multiplicity m̃ =2.252 94
Size of LCC N =453
Diameter δ =7
50-Percentile effective diameter δ0.5 =2.081 97
90-Percentile effective diameter δ0.9 =3.033 51
Median distance δM =3
Mean distance δm =2.641 74
Gini coefficient G =0.619 161
Balanced inequality ratio P =0.268 712
Relative edge distribution entropy Her =0.898 868
Power law exponent γ =1.563 30
Tail power law exponent γt =2.621 00
Tail power law exponent with p γ3 =2.621 00
p-value p =0.125 000
Degree assortativity ρ =−0.225 821
Degree assortativity p-value pρ =5.419 17 × 10−48
Clustering coefficient c =0.124 436
Spectral norm α =162.930
Algebraic connectivity a =0.264 802
Spectral separation 1[A] / λ2[A]| =1.432 48
Non-bipartivity bA =0.301 910
Normalized non-bipartivity bN =0.200 552
Algebraic non-bipartivity χ =0.293 559
Spectral bipartite frustration bK =0.008 148 41
Controllability C =8
Relative controllability Cr =0.017 660 0


Fruchterman–Reingold graph drawing

Degree distribution

Cumulative degree distribution

Lorenz curve

Spectral distribution of the adjacency matrix

Spectral distribution of the normalized adjacency matrix

Spectral distribution of the Laplacian

Spectral graph drawing based on the adjacency matrix

Spectral graph drawing based on the Laplacian

Spectral graph drawing based on the normalized adjacency matrix

Degree assortativity

Zipf plot

Hop distribution

Double Laplacian graph drawing

Delaunay graph drawing

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution


Matrix decompositions plots



[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]
[2] Jordi Duch and Alex Arenas. Community detection in complex networks using extremal optimization. Phys. Rev. E, 72(2):027104, 2005.