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First realize the chain as a random walk on a multigraph: put three parallel edges between $a,b$, two between $a,c$, and one between $b,c$. There are six edges and vertex degrees $5,4,3$. Choosing an incident edge uniformly gives exactly the stated transition probabilities. The stationary distribution of a graph random walk is the degree divided by twelve, $\pi =(5,4,3)/12$, and detailed balance follows because each edge multiplicity contributes the same flux in both directions.

For the first positive return time, Kac's lemma givesFor passage to $b$, put $h_a=k(a,b)$ and $h_c=k(c,b)$. The first-step equations are $h_a=1+(2/5)h_c$ and $h_c=1+(2/3)h_a$, since reaching $b$ stops the clock. ThusA multigraph is essential: a simple graph on three vertices cannot have six edges.

For the usual undirected graph with at least two vertices, a simple random walk chooses a neighbor uniformly at each step, so $P_{ij}=1/d_i$ when $i$ and $j$ are adjacent and zero otherwise, where $d_i$ is the degree of a vertex. The handshaking lemma gives ${\sum }_i{d}_i=2|E|$. Thus the stationary distribution of a graph random walk isFor adjacent $i,j$, ${\pi }_i{P}_{ij}=1/(2|E|)={\pi }_j{P}_{ji}$, and for nonadjacent pairs both sides vanish. By detailed balance, this is an invariant distribution and the walk started from it is a reversible Markov chain. Connectedness makes the finite walk an irreducible Markov chain, giving uniqueness of its invariant distribution. No aperiodicity assumption is needed for uniqueness, so a bipartite graph is allowed.

If the connected graph has a single isolated vertex, the usual neighbor-choice formula is undefined. With the natural absorbing convention $P_{11}=1$, its unique invariant distribution is ${\pi }_1=1$ and it is reversible. The degree-normalization formula presupposes at least one edge.