Computational and molecular characterization of Chitosan derivatives by means of graph-theoretic parameters

Zaman, Shahid; Zafar, Sonia; Ullah, Asad; Azeem, Muhammad

doi:10.1016/j.padiff.2024.100726

Cited by 1 publication

(1 citation statement)

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“…The topological index is a characteristic of graphs that remains unchanged under isomorphism, which is a property in graph theory [2][3][4][5][6]. These descriptors are distance, degree and eccentricity based, which help in understanding different networks such as biological, social networks and circuits in physics, and improve electrical circuits, identify critical components, study electrical flow and voltage distribution [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Resistance distance and sharp bounds of two-mode electrical networks

Ullah,

Salman,

Zaman

2024

Phys. Scr.

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Electrical networks are ubiquitous in our daily lives, ranging from small integrated circuits to large-scale power systems. These networks can be easily represented as graphs, where edges represent connections and vertices represent electric nodes. The concept of resistance distance originates from electrical networks, with this term used because of its physical interpretation, where every edge in a graph G is assumed to have a unit resistor. The applications of resistance distance extend to various fields such as electrical engineering, physics, and computer science. It is particularly useful in investigating the flow of electrical current in a network and determining the shortest path between two vertices. In this work, we have investigated seven different resistance distance-based indices of bipartite networks and derived general formulae for them; the sharp bounds with respect to these resistance distance indices are also identified. Additionally, we introduced a novel resistance distance topological index, the Multiplicative Eccentric Resistance Harary Index, and derived general formula for it. The sharp bounds with respect to this newly introduced index are also identified for bipartite networks.

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