View source: R/assortativity.R
| assortativity_attribute | R Documentation |
Computes assortativity with respect to a node attribute, measuring the tendency of nodes to connect to others with similar attribute values. For categorical attributes, this computes the modularity-based nominal assortativity. For numeric attributes, this computes the Pearson correlation between attribute values at edge endpoints.
assortativity_attribute(x, values, directed = NULL, digits = NULL, ...)
homophily(x, values, directed = NULL, digits = NULL, ...)
x |
Network input: matrix, igraph, network, cograph_network, or tna object. |
values |
Named vector of attribute values (names must match node names) or an unnamed vector in node order. |
directed |
Logical or NULL. If NULL (default), auto-detect. |
digits |
Integer or NULL. Round result. Default NULL. |
... |
Additional arguments passed to |
For categorical (nominal) attributes, the coefficient is:
r = \frac{\text{tr}(\mathbf{e}) - \|\mathbf{e}^2\|}{1 - \|\mathbf{e}^2\|}
where \mathbf{e} is the mixing matrix with e_{ij} = fraction of
edges connecting type i to type j.
For numeric (scalar) attributes, the coefficient is the Pearson correlation between attribute values at edge endpoints.
An object of class "cograph_assortativity" with components:
Numeric scalar: assortativity coefficient.
Character: "nominal" or "scalar".
Logical.
Integer.
Integer.
The attribute values used.
Original input.
Newman, M.E.J. (2003). Mixing patterns in networks. Physical Review E, 67(2), 026126. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1103/PhysRevE.67.026126")}
assortativity, detect_communities
adj <- matrix(c(0,1,1,0, 1,0,0,0, 1,0,0,1, 0,0,1,0), 4, 4)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")
groups <- c(A = "x", B = "x", C = "y", D = "y")
cograph::assortativity_attribute(adj, groups)
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