Deep DFS traverse on graph
You can compute the exact distribution of $\sum_{i=1}^n s_i$ using dynamic programming: for each vertex $v$, index $m \in \{0,\ldots,n\}$ and index $S \in \{0,\ldots,9m\}$, calculate the number of walks of length $m$ starting at $0$, ending at $v$, and summing to $S$. The running time is roughly $O(n^3)$ (the extra $n$ factor is due to the length of the numbers involved).

If you want an estimate, you should first find the stationary distribution of the Markov chain corresponding to a random walk on your graph. Given that, $\sum_{i=1}^n s_i$ has roughly Gaussian distribution with parameters that you can calculate from the stationary distribution; more specifically, roughly $N(n\mu, n\sigma^2)$ (this requires a version of the central limit theorem for Markov chains). This Gaussian approximation shows that the sum lies inside the interval of width $n$ centered around $n\mu$ with high probability, so taking modulo $n$ doesn't have much effect beyond shifting this interval to $\{0,\ldots,n-1\}$.