We present a comparison study between a cluster and factor graph representation of LDPC codes. In probabilistic graphical models, cluster graphs retain useful dependence between random variables during inference, which are advantageous in terms of computational cost, convergence speed, and accuracy of marginal probabilities. This study investigates these benefits in the context of LDPC codes and shows that a cluster graph representation outperforms the traditional factor graph representation.

翻译：我们针对LDPC码的聚类图表示与因子图表示展开了一项对比研究。在概率图模型中，聚类图在推理过程中保留了随机变量间有用的依赖关系，这种特性在计算成本、收敛速度以及边缘概率精确度方面具有优势。本研究从LDPC码的视角考察了这些优势，表明聚类图表示优于传统的因子图表示。