| rdfs:comment
| - Proximity modeling, for our purposes here, will be taken as more or less synonymous with graph embedding. A graph is a mathematically defined object. The definition of a particular graph includes two types of constituent objects. These are called vertices and edges. Graphs have this nomenclature in common with polyhedra (which also possess faces). When envisioning graphs, it may do to imagine the vertices as dots, or points; and the edges as line segments. Each edge has two ends, and each end is attached to one of the graph's vertices. With graphs, unlike with polyhedra, there is no general assumption that these lines are "straight." The important thing about graphs is the question of "what connects to what." For this reason, graph theory has found applications in practices that have to do
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| abstract
| - Proximity modeling, for our purposes here, will be taken as more or less synonymous with graph embedding. A graph is a mathematically defined object. The definition of a particular graph includes two types of constituent objects. These are called vertices and edges. Graphs have this nomenclature in common with polyhedra (which also possess faces). When envisioning graphs, it may do to imagine the vertices as dots, or points; and the edges as line segments. Each edge has two ends, and each end is attached to one of the graph's vertices. With graphs, unlike with polyhedra, there is no general assumption that these lines are "straight." The important thing about graphs is the question of "what connects to what." For this reason, graph theory has found applications in practices that have to do with connectivity, such as electrical schematics and utility grids. For the purpose of this discussion, the embedding of graphs will be taken to mean the drawing of graphs in (typically) two- or three-dimensional format, with a "goal" of keeping the edges as short as possible. The pubwan literature includes many references to proximity modeling because it is seen as a way to position in virtual space. This is seen as a worthwhile endeavour because the exploration of conceivable arrangements of economic goods in "space" is seen as a possible fruitful area for knowledge discovery. This site includes some public domain LISP code for exploring graph embedding. These routines are strictly "brute force" and should not be regarded as representing current knowledge about graph embedding or proximity modeling. If you decide to load them into your favorite Common Lisp environment, make sure you have both files, and read the comments in order to understand the data structure used to represent a graph. You can get these files at the following locations: 1.
* 2.
* The above have been superseded by: The search continues for more elegant and/or effective solutions, as well as a "front end" to interface with a pubwan database or interactive data entry (see volunteer information).
- Proximity modeling, for our purposes here, will be taken as more or less synonymous with graph embedding. A graph is a mathematically defined object. The definition of a particular graph includes two types of constituent objects. These are called vertices and edges. Graphs have this nomenclature in common with polyhedra (which also possess faces). When envisioning graphs, it may do to imagine the vertices as dots, or points; and the edges as line segments. Each edge has two ends, and each end is attached to one of the graph's vertices. With graphs, unlike with polyhedra, there is no general assumption that these lines are "straight." The important thing about graphs is the question of "what connects to what." For this reason, graph theory has found applications in practices that have to do with connectivity, such as electrical schematics and utility grids. For the purpose of this discussion, the embedding of graphs will be taken to mean the drawing of graphs in (typically) two- or three-dimensional format, with a "goal" of keeping the edges as short as possible. This is seen as a worthwhile endeavour because the exploration of conceivable arrangements of economic goods in "space" is seen as a possible fruitful area for knowledge discovery. Here is some public domain LISP code for exploring graph embedding. These routines are strictly "brute force" and should not be regarded as representing current knowledge about graph embedding or proximity modeling. If you decide to load them into your favorite Common Lisp environment, make sure you have both files, and read the comments in order to understand the data structure used to represent a graph. You can get these files at the following locations: 1.
* 2.
* The above have been superseded by: The search continues for more elegant and/or effective solutions, as well as a "front end" to interface with a public domain database or interactive data entry (see volunteer information).
- Proximity modeling, for our purposes here, will be taken as more or less synonymous with graph embedding. A graph is a mathematically defined object. The definition of a particular graph includes two types of constituent objects. These are called vertices and edges. Graphs have this nomenclature in common with polyhedra (which also possess faces). When envisioning graphs, it may do to imagine the vertices as dots, or points; and the edges as line segments. Each edge has two ends, and each end is attached to one of the graph's vertices. With graphs, unlike with polyhedra, there is no general assumption that these lines are "straight." The important thing about graphs is the question of "what connects to what." For this reason, graph theory has found applications in practices that have to do with connectivity, such as electrical schematics and utility grids. For the purpose of this discussion, the embedding of graphs will be taken to mean the drawing of graphs in (typically) two- or three-dimensional format, with a "goal" of keeping the edges as short as possible. Proximity modeling is seen as a way to position in virtual space. This is seen as a worthwhile endeavour because the exploration of conceivable arrangements of economic goods in "space" is seen as a possible fruitful area for knowledge discovery.
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