A wide range of important problems in machine learning, expert system, social network analysis, bioinformatics and information theory can be formulated as a maximum a-posteriori (MAP) inference problem on statistical relational models. While off-the-shelf inference algorithms that are based on local search and message-passing may provide adequate solutions in some situations, they frequently give poor results when faced with models that possess high-density networks. Unfortunately, these situations always occur in models of real-world applications. As such, accurate and scalable maximum a-posteriori (MAP) inference on such models often remains a key challenge. In this paper, we first introduce a novel family of extended factor graphs that are parameterized by a smoothing parameter χ ∈ [0,1]. Applying belief propagation (BP) message-passing to this family formulates a new family of W eighted S urvey P ropagation algorithms (WSP- χ ) applicable to relational domains. Unlike off-the-shelf inference algorithms, WSP- χ detects the “backbone” ground atoms in a solution cluster that involve potentially optimal MAP solutions: the cluster backbone atoms are not only portions of the optimal solutions, but they also can be exploited for scaling MAP inference by iteratively fixing them to reduce the complex parts until the network is simplified into one that can be solved accurately using any conventional MAP inference method. We also propose a lazy variant of this WSP- χ family of algorithms. Our experiments on several real-world problems show the efficiency of WSP- χ and its lazy variants over existing prominent MAP inference solvers such as MaxWalkSAT, RockIt, IPP, SP-Y and WCSP.

中文翻译：

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利用集群骨干改善统计关系模型中的 MAP 推理

机器学习、专家系统、社交网络分析、生物信息学和信息论中的许多重要问题都可以表述为统计关系模型上的最大后验 (MAP) 推理问题。虽然基于本地搜索和消息传递的现成推理算法在某些情况下可能提供足够的解决方案，但在面对拥有高密度网络的模型时，它们经常给出较差的结果。不幸的是，这些情况总是发生在实际应用程序的模型中。因此，对此类模型进行准确且可扩展的最大后验 (MAP) 推理通常仍然是一个关键挑战。在本文中，我们首先介绍了一个新的扩展因子图系列，这些图由平滑参数 χ ∈ [0,1] 参数化。将信念传播 (BP) 消息传递应用于该系列，制定了适用于关系域的新系列 W 八式调查传播算法 (WSP-χ)。与现成的推理算法不同，WSP-χ 检测涉及潜在最优 MAP 解决方案的解决方案集群中的“主干”基础原子：集群主干原子不仅是最佳解决方案的一部分，而且还可以用于通过迭代修复它们以减少复杂部分来缩放 MAP 推理，直到网络被简化为可以使用任何传统 MAP 推理方法准确求解的网络。我们还提出了这个 WSP-χ 系列算法的惰性变体。