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Belief propagation-based positioning is a promising technique to provide accurate geographical locations for location-based services without central coordination, but its performance can be severely affected by the choice of anchor locations. To solve this problem, we formulate an epidemic model to capture the propagation process, by which the localization time can be upper-bounded by functions of topological characteristics of the agent network. With this knowledge, we design a graph-based anchor deployment strategy that ranks the agents by its topological importance, and deploy anchors to best cover the most important ones. Numerical results show that the proposed graph-based approach outperforms existing solutions in both convergence speed and localization errors, providing a practical solution to BP-based localization schemes.