View source: R/incidence.from.adjacency.R
incidence.from.adjacency | R Documentation |
incidence.from.adjacency
generates an incidence matrix from an adjacency matrix or network using
a given generative model
incidence.from.adjacency( G, k = 1, p = 1, blau.param = c(2, 1, 10), maximal = TRUE, model = "team", class = NULL, narrative = TRUE )
G |
A symmetric, binary adjacency matrix of class |
k |
integer: Number of artifacts to generate |
p |
numeric: Tuning parameter for artifacts, 0 <= p <= 1 |
blau.param |
vector: Vector of parameters that control blau space in the organizations model (see details) |
maximal |
boolean: Should teams/clubs models be seeded with maximal cliques? |
model |
string: Generative model, one of c("team", "club", "org") (see details) |
class |
string: Return object as |
narrative |
boolean: TRUE if suggested text & citations should be displayed. |
Given a unipartite network composed of i agents (i.e. nodes) that can be represented by an i x i adjacency
matrix, incidence.from.adjacency
generates a random i x k incidence matrix that indicates whether agent
i is associated with artifact k. Generative models differ in how they conceptualize artifacts and how
they associate agents with these artifacts.
The Team Model (model == "team"
) mirrors a team formation process, where each artifact represents a new team
formed from the incumbants of a prior team (with probability p
) and newcomers (with probability 1-p
).
The Club Model (model == "club"
) mirrors a social club formation process, where each artifact represents
a social club. Club members attempt to recruit non-member friends, who join the club if it would have a
density of at least p
.
The Organizations Model (model == "org"
) mirrors an organization (the artifact) recruiting members from social
space, where those within the organization's niche join with probability p
, and those outside the niche join
with probability 1-p
. blau.param
is a vector containing three values that control the characteristics of the
blau space. The first value is the space's dimensionality. The second two values are shape parameters of a Beta
distribution that describes niche sizes. The default is a two-dimensional blau space, with organization niche
sizes that are strongly positively skewed (i.e., many specialist organizations, few generalists).
An incidence matrix of class matrix
or Matrix
, or a bipartite graph of class igraph.
Neal, Z. P. 2023. The duality of networks and groups: Models to generate two-mode networks from one-mode networks. Network Science.
G <- igraph::erdos.renyi.game(10, .4) I <- incidence.from.adjacency(G, k = 1000, p = .95, model = "team", narrative = TRUE)
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