On special properties of some quasi-metrics

In this paper we consider some questions of the theory and practice of mean first passage time quasi-metric, a generalized metric structure closely related to ergodic homogeneous Markov chains.
The introduction considers the history of the problem and provides an overview of the main ideas and results presented in the article.
The first section gives the basic concepts of the theory of Markov chains. In fact, a Markov chain is a mathematical model of some random process describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
The second section collects the basic definitions needed to consider the role of graph models in the presentation and study of ergodic homogeneous Markov chains. The Markov chain can be depicted as an oriented weighted graph of transitions whose vertices correspond to the states of the chain and the arcs correspond to the transitions between them. The process will be ergodic if this weighted oriented graph is weakly connected, and the largest common divisor of the lengths of all its cycles is equal to $1$. On the other hand, any connected graph can be used as a basis for building a model of the simplest Markov chain: if a vertex $i$ has degree $k$, all incident edges are converted into arcs with the weights $\frac{1}{k}$.
In the third section the definition of the mean first passage time for an ergodic homogeneous Markov chain is given. There are several ways to build the corresponding matrix. The algorithm of finding the mean first passage time is analyzed in detail by using converging trees of the oriented graph, related to the transition matrix of the ergodic homogeneous Markov chain. Related recurrent procedure is described.
In the fourth section, a mean first passage time is analyzed as the quasi-metric $m$ of mean first passage time on the vertices $V =\{1, 2,..., n\}$ of the oriented graph corresponding to the transition matrix of a given ergodic homogeneous Markov chain: $m(i, j)$ is the expected number of steps (arcs) for random wandering on the oriented graph $\Gamma$, starting at $i$, to reach $j$ for the first time. In particular, the quasi-metric of mean first passage time for the simple random walking on a connected unweighted graph $G$, in which there is an equal probability of moving from any vertex to any adjacent vertex, is a weighted quasi-metric, i.e., there exists a weight function $w: V\rightarrow\mathbb {R}_ {\ge 0}$, such that $ m(i,j) + w_i= m(j,i)+ w_j $ for all $i, j\in V$.
We consider also some connections of the mean first passage time quasi-metric to other metric structures on graphs (in particular to $\alpha $-metric forest and its relatives), which are less studied, but not less interesting.
Finally, the fifth section deals with examples of the construction of mean first passage time quasi-metrics. In addition to illustrating the "graphical" procedure of building the matrix $M$, recurrent research algorithms are presented and the resulting generalized metric structures are analyzed.