Study of Neural Network Algorithm for Straight-Line Drawings of Planar Graphs
This addresses the need for efficient graph drawing methods in applications like VLSI layouts, where reduced computational demands can lower costs, though it appears incremental as it builds on existing neural network techniques.
The paper tackles the problem of generating straight-line drawings of planar graphs with minimal computational resources, introducing a new layout method using Self-Organizing Map (SOM) and Inverse Self-Organized Map (ISOM) that consumes little resources and avoids heavy preprocessing.
Graph drawing addresses the problem of finding a layout of a graph that satisfies given aesthetic and understandability objectives. The most important objective in graph drawing is minimization of the number of crossings in the drawing, as the aesthetics and readability of graph drawings depend on the number of edge crossings. VLSI layouts with fewer crossings are more easily realizable and consequently cheaper. A straight-line drawing of a planar graph G of n vertices is a drawing of G such that each edge is drawn as a straight-line segment without edge crossings. However, a problem with current graph layout methods which are capable of producing satisfactory results for a wide range of graphs is that they often put an extremely high demand on computational resources. This paper introduces a new layout method, which nicely draws internally convex of planar graph that consumes only little computational resources and does not need any heavy duty preprocessing. Here, we use two methods: The first is self organizing map known from unsupervised neural networks which is known as (SOM) and the second method is Inverse Self Organized Map (ISOM).