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Every bounded discrete harmonic function on ${\mathbb{Z}}^d$ is constant. After rescaling its range to $[0,1]$, place it in the compact convex set of all such functions; translation invariance and the mean-value identity force every extreme point to be constant, and the Krein-Milman theorem finishes the proof.

A real function $h$ on the vertices of a graph of an unweighted locally finite graph is a discrete harmonic function at $u$ when $\mathrm{deg}(u)h(u)={\sum }_{v\sim u}h(v)$, equivalently $Lh(u)=0$ for the Graph Laplacian. For positive degree this says its value is the average over its graph neighbours; at an isolated vertex of a graph the Laplacian condition is vacuous. On an electrical network the corresponding equation uses conductance weights and the Weighted graph Laplacian. Voltages away from sources and sinks, and hitting probabilities away from their prescribed boundaries, are discrete harmonic functions.

A harmonic function assigns a value to each state of a Markov chain such that one transition leaves its expected value unchanged. This turns a question about many possible future sample paths, such as which endpoint is reached first, into a local averaging rule. For a transition matrix $P$, a real-valued function $h$ is harmonic at state $i$ whenHere ${\mathbb{E}}_i$ means expected value conditional on starting at $X_0=i$; on a countable state space the sum must be absolutely convergent. If this holds at every state, write $Ph=h$. At an absorbing state the equation is automatic, so prescribed boundary conditions supply information that the averaging rule cannot.

For a finite Markov chain, choose a boundary set $B$ that can be reached from every state, and assign boundary values $b$. If $T_B$ is its first hitting time, the harmonic extension isChoosing $b$ to be the indicator function of one target within $B$ gives a hitting probability. This is a discrete Dirichlet problem: enforce the averaging rule outside $B$ and fix the values on $B$. See Hao Wu's MIT lecture 8, harmonic extension, slide 5. The construction extends the mean-value idea of a harmonic function; for a random walk on a graph it is a discrete harmonic function.