Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model (PGM) where the feature connections are modeled using an undirected graph and the edge weights show the partial correlation strength between the features. Since the CI graphs capture direct dependence between features, they have been garnering increasing interest within the research community for gaining insights into the systems from various domains, in particular discovering the domain topology. In this work, we propose algorithms for performing knowledge propagation over the CI graphs. Our experiments demonstrate that our techniques improve upon the state-of-the-art on the publicly available Cora and PubMed datasets.

翻译：条件独立（CI）图是一种特殊类型的概率图模型（PGM），其中特征连接通过无向图建模，边权表示特征之间的偏相关强度。由于CI图能够捕捉特征间的直接依赖关系，近年来在学术界引起了越来越多的关注，用于从不同领域系统中获取洞察，尤其是发现领域拓扑结构。在本文中，我们提出了在CI图上进行知识传播的算法。实验表明，我们的技术在公开可用的Cora和PubMed数据集上超越了当前最先进的方法。