— Loss networks provide a powerful tool for the analysis and design of many communication and networking systems. It is well known that a large number of loss networks have product-form steady-state probabilities. However, for most networks of practical interest, evaluating the system performance is a difficult task due to the presence of a normalization constant. In this paper, we present a new framework based on probabilistic graphical models to tackle this task. Specifically, we propose to use factor graphs to model the stationary distribution of a network. Based on the factor graph model, we can easily derive recursive formulas for symmetric networks. Most importantly, for networks with arbitrary topology, we can apply efficient message-passing algorithms like the sum-product algorithm to compute the exact or approximate marginal distributions of all state variables and the related performance measures such as call blocking probabilities. Through extensive numerical experiment...