线性化和单遍置信度传播
Linearized and Single-Pass Belief Propagation
摘要：
我们如何分辨社交网络中的账户是真是假？我们如何区分哪些账户属于自由派、保守派或中间派用户？通常，我们可以回答这些问题，并根据邻居的标签和适当的同质性（“物以类聚”）或异质性（“异性相吸”）假设来标记网络中的节点。用于这种推理的最广泛使用的方法之一是置信传播（BP），其迭代地将来自具有显式标签的几个节点的信息传播到整个网络，直到收敛。然而，BP的一个主要问题是，在有循环的图中没有已知的精确收敛保证。本文介绍了线性化置信传播（LinBP），BP的线性化，允许通过直观的矩阵方程的封闭形式的解决方案，因此，与收敛保证。它处理homophily，heterophily，以及在多类设置中出现的更一般的情况。另外，它允许在SQL中进行紧凑的实现。本文还介绍了单通道置信传播（SBP），一个“本地化”版本的LinBP，传播信息在每个边缘最多一次，最终的类分配只取决于最近的标记邻居。此外，SBP允许在动态网络中进行快速增量更新。我们的运行时实验表明，LinBP和SBP的数量级快于标准
英文摘要：
How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily ("birds of a feather flock together") or heterophily ("opposites attract"). One of the most widely used methods for this kind of inference is Belief Propagation (BP) which iteratively propagates the information from a few nodes with explicit labels throughout a network until convergence. One main problem with BP, however, is that there are no known exact guarantees of convergence in graphs with loops. This paper introduces Linearized Belief Propagation (LinBP), a linearization of BP that allows a closed-form solution via intuitive matrix equations and, thus, comes with convergence guarantees. It handles homophily, heterophily, and more general cases that arise in multi-class settings. Plus, it allows a compact implementation in SQL. The paper also introduces Single-pass Belief Propagation (SBP), a "localized" version of LinBP that propagates information across every edge at most once and for which the final class assignments depend only on the nearest labeled neighbors. In addition, SBP allows fast incremental updates in dynamic networks. Our runtime experiments show that LinBP and SBP are orders of magnitude faster than standard
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
