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We mimic random nanowire networks by the homogeneous, isotropic, and randomdeposition of conductive zero-width sticks onto an insulating substrate. Thenumber density (the number of objects per unit area of the surface) of thesesticks is supposed to exceed the percolation threshold, i.e., the system underconsideration is a conductor. To identify any current-carrying part (thebackbone) of the percolation cluster, we have proposed and implemented amodification of the well-known wall follower algorithm -- one type of mazesolving algorithm. The advantage of the modified algorithm is itsidentification of the whole backbone without visiting all the edges. Thecomplexity of the algorithm depends significantly on the structure of the graphand varies from $O\left(\sqrt{N_\text{V}}\right)$ to $\Theta(N_\text{V})$. Thealgorithm has been applied to backbone identification in networks withdifferent number densities of conducting sticks. We have found that (i) fornumber densities of sticks above the percolation threshold, the strength of thepercolation cluster quickly approaches unity as the number density of thesticks increases; (ii) simultaneously, the percolation cluster becomesidentical to its backbone plus simplest dead ends, i.e., edges that areincident to vertices of degree 1. This behavior is consistent with thepresented analytical evaluations.

Random nanowire networks: Identification of a current-carrying subset of wires using a modified wall follower algorithm