On Entropy of Some Fractal Structures

Abstract

Shannon entropy, also known as information entropy or entropy, measures the uncertainty or randomness of probability distribution. Entropy is measured in bits, quantifying the average amount of information required to identify an event from the distribution. Shannon’s entropy theory initiates graph entropies and develops information-theoretic magnitudes for structural computational evidence of organic graphs and complex networks. Graph entropy measurements are valuable in several scientific fields, such as computing, chemistry, biology, and discrete mathematics. In this study, we investigate the entropy of fractal-type networks by considering cycle, complete, and star networks as base graphs using degree-based topological indices.

Computing Certain Topological Indices of Silicate Triangle Fractal Network Modeled by the Sierpiński Triangle Network

“…Various Zagreb indices have been studied for a Sierpiński triangle networks by [19]. Various authors [11,20,21] have been discussed the entropy of fractal-type networks, topological properties of Sierpiński network, and the fractal dimension of Sierpiński triangle network respectively. The authors [22] have contributed few degrees based topological indices for the Sierpiński rhombus (SR n ) and Koch snowflake (KS n ) graphs.…”