galois: Galois Derivations Between Subsets

In multiplex: Algebraic Tools for the Analysis of Multiple Social Networks

Galois Derivations Between Subsets

Description

Function to perform Galois derivations between partially ordered subsets

Usage

galois(x, labeling = c("full", "reduced"), sep, valued, scl, 
    sep2)

Arguments

x	a data frame with objects and attributes
labeling	whether the derivations should be full for full labeling reduced for reduced labeling
sep	(optional) pair separator used for the pairwise relations
valued	(logical) whether the galois derivation is on a many-valued formal context
scl	(optional, only for valued) the scale to be used in the galois derivation
sep2	(optional, only for valued) the separator in the formal concept

Details

Galois derivations (or connections) are mappings between families of partially ordered subsets of elements. Such derivations are useful to analyze the structure of both subsets, which in a social network are typically the actors and their corresponding affiliations or events. That is, two-mode networks, but also a group of objects with a list of different attributes as used in formal concept analysis.

Value

A labelled list with Galois derivations of objects and attributes

Note

Full labeling implies first objects and then attributes, whereas the reduced option is given the other way around.

Author(s)

Antonio Rivero Ostoic

References

Ganter, B. and R. Wille Formal Concept Analysis – Mathematical Foundations. Springer. 1996.

See Also

partial.order, diagram, fltr.

Examples

# create a data frame
dfr <- data.frame(x=1:3, y=5:7)

# find galois derivations
galois(dfr)

multiplex documentation built on Sept. 30, 2024, 5:07 p.m.