A random walk on a graph $G$ with vertices $\{1,2,\ldots,n\}$ has transition matrix $P(i,j)=\frac{n_{ij}}{d_i}$ where $n_{ij}$ is 1 for $i,j$ being neighbors and 0 otherwise and $d_i$ is the degree of $i$. This transition matrix is reversible with respect to the measure $\pi(i)=\frac{d_i}{\sum_i d_i}=\frac{d_i}{2m}$, where $m$ is the number of edges:
$\pi(i)P(i,j)=\pi(j)P(j,i)$.
This easily implies $\pi$ is a stationary measure of the Markov chain: $\pi P= \pi$, in that $\sum_i \pi(i)P(i,j)=\sum_i\pi(j)P(j,i)=\pi(j)$. Assuming the Markov chain is both irreducible and aperiodic, this implies we have convergence to the stationary distribution. Note that it is not quite "uniform" in that not all points are equally likely, rather each point is as likely as it's degree.