On Computing Eccentricity Based Topological Invariants of Tickysim SpiNNaker Model Sheet

The study of topological invariants provides a dynamic way to establish a correlation with a structural graph and its attributes. In this form, nodes stand in for the vertices, and edges show the connections between them. In chemical graph theory substances are numerically modeled using topological indices to gain understanding of their physicochemical properties. Its eccentricity is the maximum distance of a random vertex, \(\check{a}\), and vertex, \(\check{e}\), with minimum path. In this work, we compute the Tickysim SpiNNaker Model Sheet's eccentricity based indices. Further, we formulate analytically closed equations of these distance based topological invariants which support in examining the basic structural topology. The data analysis with graphs at certain points are developed using machine learning algorithms.