# Prediction nullity of graph using data mining

## Prediction nullity of graph using data mining

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## Keywords:

Nullity of a graph, Machine learning, Graph, Eigenvalue## Abstract

Nullity computation is widely used to determine the stability of a chemical molecule. Mainly, a molecule is presented as a graph, and the graph nullity value clarifies the strength of the molecule. Some formulas for specific graphs help us compute the nullity value, but it is challenging to remember the formula of each particular graph. However, another formula for calculating the nullity value is based on the graph rank. Nevertheless, processing time would be increased by the growth of the number of vertices of graphs. This paper suggests machine learning methods for computing the nullity value of a given graph. We leveraged random graph generation methods to collect many graph instances. Then, the experimental results on the collected dataset offer accuracy of 97.0878% for binary classification and 94.56% for value prediction.

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