features | R Documentation |
Measures of network topological features
network_core(.data, membership = NULL)
network_richclub(.data)
network_factions(.data, membership = NULL)
network_modularity(.data, membership = NULL, resolution = 1)
network_smallworld(.data, method = c("omega", "sigma", "SWI"), times = 100)
network_scalefree(.data)
network_balance(.data)
.data |
An object of a
|
membership |
A vector of partition membership. |
resolution |
A proportion indicating the resolution scale. By default 1. |
method |
There are three small-world measures implemented:
|
times |
Integer of number of simulations. |
network_core()
: Returns correlation between a given network
and a core-periphery model with the same dimensions.
network_richclub()
: Returns rich-club coefficient
network_factions()
: Returns correlation between a given network
and a component model with the same dimensions.
If no 'membership' vector is given for the data,
node_kernaghinlin()
is used to obtain a partition into two groups.
network_modularity()
: Returns modularity based on nodes' membership
in pre-defined clusters.
network_smallworld()
: Returns small-world metrics for one- and
two-mode networks.
Small-world networks can be highly clustered and yet
have short path lengths.
network_scalefree()
: Returns the exponent of the fitted
power-law distribution.
Usually an exponent between 2 and 3 indicates a power-law
distribution.
network_balance()
: Returns the structural balance index on
the proportion of balanced triangles,
ranging between 0
if all triangles are imbalanced and
1
if all triangles are balanced.
Modularity measures the difference between the number of ties within each community from the number of ties expected within each community in a random graph with the same degrees, and ranges between -1 and +1. Modularity scores of +1 mean that ties only appear within communities, while -1 would mean that ties only appear between communities. A score of 0 would mean that ties are half within and half between communities, as one would expect in a random graph.
Modularity faces a difficult problem known as the resolution limit (Fortunato and Barthélemy 2007). This problem appears when optimising modularity, particularly with large networks or depending on the degree of interconnectedness, can miss small clusters that 'hide' inside larger clusters. In the extreme case, this can be where they are only connected to the rest of the network through a single tie.
{signnet}
by David Schoch
Borgatti, Stephen P., and Martin G. Everett. 2000. “Models of Core/Periphery Structures.” Social Networks 21(4):375–95. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0378-8733(99)00019-2")}
Murata, Tsuyoshi. 2010. Modularity for Bipartite Networks. In: Memon, N., Xu, J., Hicks, D., Chen, H. (eds) Data Mining for Social Network Data. Annals of Information Systems, Vol 12. Springer, Boston, MA. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-1-4419-6287-4_7")}
Watts, Duncan J., and Steven H. Strogatz. 1998. “Collective Dynamics of ‘Small-World’ Networks.” Nature 393(6684):440–42. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/30918")}.
Telesford QK, Joyce KE, Hayasaka S, Burdette JH, Laurienti PJ. 2011. "The ubiquity of small-world networks". Brain Connectivity 1(5): 367–75. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1089/brain.2011.0038")}.
Neal Zachary P. 2017. "How small is it? Comparing indices of small worldliness". Network Science. 5 (1): 30–44. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1017/nws.2017.5")}.
network_transitivity()
and network_equivalency()
for how clustering is calculated
Other measures:
between_centrality
,
close_centrality
,
closure
,
cohesion()
,
degree_centrality
,
eigenv_centrality
,
heterogeneity
,
hierarchy
,
holes
network_core(ison_adolescents)
network_core(ison_southern_women)
network_richclub(ison_adolescents)
network_factions(mpn_elite_mex)
network_factions(ison_southern_women)
network_modularity(ison_adolescents,
node_kernighanlin(ison_adolescents))
network_modularity(ison_southern_women,
node_kernighanlin(ison_southern_women))
network_smallworld(ison_brandes)
network_smallworld(ison_southern_women)
network_scalefree(ison_adolescents)
network_scalefree(generate_scalefree(50, 1.5))
network_scalefree(create_lattice(100))
network_balance(ison_marvel_relationships)
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