On b-Coloring Analysis of Graphs: An Application to Spatial-Temporal Graph Neural Networks for Multi-Step Time Series Forecasting of Soil Moisture and pH in Companion Farming
DOI:
https://doi.org/10.29020/nybg.ejpam.v17i4.5409Keywords:
$b-$Coloring, companion farming, multi-step time series forecastingAbstract
Let $G$ be a pair of two sets $(V,E)$ with vertex set $V$ and edge set $E$. A proper coloring of a graph $G$ is a vertex coloring of it such that no two adjacent vertices in $G$ have the same color. By $b-$Coloring, we define a coloring of the vertex of $G$ such that each color class has at least one vertex that adjacent with all other color classes. The $b-chromatic$ number of graph $G$, denoted by $\varphi(G),$ is the largest integer $k$ such that graph $G$ has $b-$Coloring with $k$ colors. In this paper, we will explore some new lemmas or theorems regarding to $\varphi(G)$. Furthermore, to see the robust application of $b-$Coloring of graph, at the end of this paper we will illustrate the implementation of $b-$Coloring on spatial temporal graph neural networks (STGNN) multi-step time series forecasting on soil moisture and $pH$ of companion farming.
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Copyright (c) 2024 Arika Indah Kristiana, Elisa Rachmasari, Dafik ., Ika Hesti Agustin, Indah Luthfiyah Mursyidah, Ridho Alfarisi
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