Devi Eka Wardani Meganingtyas, Ibnu Hadi, Qorry Meidianingsih, Samir Naqos, Bhayu Phermana Sachty Muktar
Rainbow antimagic coloring is a research topic on graphs that can be applied in everyday life. The graph neural network itself is a deep learning architecture that is used to solve machine learning problems on data in the form of graphs. Graph Neural Networks (GNNs) are a topic that researchers are currently discussing. GNNs are capable of performing tasks that Convolutional Neural Networks cannot do. By looking at existing literature, this research will examine the use of Rainbow Antimagic Coloring of Graph and Graph Neural Networks. The integration of Rainbow Antimagic Coloring of Graph and Graph Neural Networks is expected to be able to solve problems in life. Based on the results obtained from existing literature, the rainbow antimagic coloring concept can be combined with the neural network concept to solve real-life problems, including irrigation systems and supply chain management. © 2024 Author(s).
Department of Mathematics, Universitas Negeri Jakarta, Jakarta, Indonesia; Universiti Teknologi Malaysia, Johor Bahru, Johor Bahru, Johor, 81310, Malaysia
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