She gives an amazingly clear description of why, on a random walk on an undirected graphs, the stationary distribution puts probability on each node proportional to its degree.
Houston-Edwards also relies without proof on the following fact: The expected time to start and return to a vertex v is 1/p where p is the probability given to v in the stationary distribution. Why should that be true?
Didn't seem so obvious to me, so I looked it up. Here is an informal proof:
Let V1 be the random variable that is the expected length of time to get from v back to v. Let's take that walk again and let V2 be the expected length of time to get from v back to v the second time, and so on. Let An=V1+V2+..+Vn, the random variable representing the number of transitions taken to return to v n times. In a Markov chain each Vi is distributed the same so E(An) = n E(V1).
As n gets large the Markov chain approaches the stationary distribution and will be in state v about a p fraction of the time. After E(An) transitions we should return to v about p E(An) times, where we actually return n times. So we have p E(An) approaches n, or p n E(V1) approaches n and the only way this could happen is if E(V1)=1/p.
The fact you're referring to is proved as Prop 1.14 in Levin, Peres and Wilmer's book "Rapid Mixing in Markov Chains" (2nd edition). They show the slightly more general fact that if
\pi(y) = E_z (number of visits to y before returning to z),
where E_z denotes the expectation when the chain begins from z, then \pi(y) is a stationary measure for the transition matrix P (assuming it is irreducible and the state space is finite).
To make a probability distribution out of this they divide by the expected return time from z to z. In this if you consider \pi(z)/E_z(first return to z) that is naturally 1/E_z(first return to z), which proves the result.