The problem of PIR in graph-based replication systems has received significant attention in recent years. A systematic study was conducted by Sadeh, Gu, and Tamo, where each file is replicated across two servers and the storage topology is modeled by a graph. The PIR capacity of a graph $G$, denoted by $\mathcal{C}(G)$, is defined as the supremum of retrieval rates achievable by schemes that preserve user privacy, with the rate measured as the ratio between the file size and the total number of bits downloaded. This paper makes the following key contributions. (1) The complete graph $K_N$ has emerged as a central benchmark in the study of PIR over graphs. The asymptotic gap between the upper and lower bounds for $\mathcal{C}(K_N)$ was previously 2 and was only recently reduced to $5/3$. We shrink this gap to $1.0444$, bringing it close to resolution. More precisely, (i) Sadeh, Gu, and Tamo proved that $\mathcal{C}(K_N)\le 2/(N+1)$ and conjectured this bound to be tight. We refute this conjecture by establishing the strictly stronger bound $\mathcal{C}(K_N) \le \frac{1.3922}{N}.$ We also improve the upper bound for the balanced complete bipartite graph $\mathcal{C}(K_{N/2,N/2})$. (ii) The first lower bound on $\mathcal{C}(K_N)$ was $(1+o(1))/N$, which was recently sharpened to $(6/5+o(1))/N$. We provide explicit, systematic constructions that further improve this bound, proving $\mathcal{C}(K_N)\ge(4/3-o(1))/N,$ which in particular implies $\mathcal{C}(G) \ge (4/3-o(1))/|G|$ for every graph $G$. (2) We establish a conceptual bridge between deterministic and probabilistic PIR schemes on graphs. This connection has significant implications for reducing the required subpacketization in practical implementations and is of independent interest. We also design a general probabilistic PIR scheme that performs particularly well on sparse graphs.