Complex network analysis of the thermal lens signal: a Markov model approach

The paper reports a novel, to the best of my knowledge, complex network-based Markov model approach to analyze the thermal lens (TL) signal. The complex network is constructed by segmenting the experimental and fitted TL signals into three regions. Here, a new parameter, degree of fluctuation, is introduced to analyze the Markov transition probability matrix (M) and the increase of system enthalpy leading to increased Brownian motion. The spread of data about the diagonal elements of M distinguishes the experimental and fitted data and appears as an increased number of edges in the complex network. Thus, the system's network displays the transient heat flow giving information about the Brownian motion in the medium.