triadic-ergmHint {ergm} | R Documentation |
Network with strong clustering (triad-closure) effects
Description
The network has a high clustering coefficient. This typically results in alternating between the Tie-Non-Tie (TNT) proposal and a triad-focused proposal along the lines of that of Wang and Atchadé (2013).
Usage
# triadic(triFocus = 0.25, type="OTP")
# .triadic(triFocus = 0.25, type = "OTP")
Arguments
triFocus |
A number between 0 and 1, indicating how often triad-focused proposals should be made relative to the standard proposals. |
type |
A string indicating the type of shared partner or path to be considered for directed networks: |
Shared partner types
While there is only one shared partner configuration in the undirected
case, nine distinct configurations are possible for directed graphs, selected
using the type
argument. Currently, terms may be defined with respect to
five of these configurations; they are defined here as follows (using
terminology from Butts (2008) and the relevent
package):
Outgoing Two-path (
"OTP"
): vertexk
is an OTP shared partner of ordered pair(i,j)
iffi \to k \to j
. Also known as "transitive shared partner".Incoming Two-path (
"ITP"
): vertexk
is an ITP shared partner of ordered pair(i,j)
iffj \to k \to i
. Also known as "cyclical shared partner"Reciprocated Two-path (
"RTP"
): vertexk
is an RTP shared partner of ordered pair(i,j)
iffi \leftrightarrow k \leftrightarrow j
.Outgoing Shared Partner (
"OSP"
): vertexk
is an OSP shared partner of ordered pair(i,j)
iffi \to k, j \to k
.Incoming Shared Partner (
"ISP"
): vertexk
is an ISP shared partner of ordered pair(i,j)
iffk \to i, k \to j
.
By default, outgoing two-paths ("OTP"
) are calculated. Note that Robins et al. (2009)
define closely related statistics to several of the above, using slightly different terminology.
.triadic()
versus triadic()
If given a bipartite network, the dotted form will skip silently, whereas the plain form will raise an error, since triadic effects are not possible in bipartite networks. The dotted form is thus suitable as a default argument when the bipartitedness of the network is not known a priori.
References
Wang J, Atchadé YF (2013). “Approximate Bayesian Computation for Exponential Random Graph Models for Large Social Networks.” Communications in Statistics - Simulation and Computation, 43(2), 359–377. ISSN 1532-4141, doi:10.1080/03610918.2012.703359.
See Also
ergmHint
for index of constraints and hints currently visible to the package.
Keywords
dyad-dependent