论文标题
从两部分网络中提取分层骨架
Extracting hierarchical backbones from bipartite networks
论文作者
论文摘要
我们提出了一种从两部分网络中提取分层骨架的方法。我们的方法利用了以下观察结果：在两部分网络中的两个节点之间的层次关系通常表现为从其他节点集观察与它们连接的条件概率中的不对称性。我们的方法估计了一对节点之间分层关系的重要性和方向，从而提供了一种灵活的方法来识别网络的基本部分。使用半合成基准测试，我们表明我们的方法在识别种植的层次结构的同时，在提供更灵活的同时，我们的方法优于现有方法。应用我们的方法在经验数据集中的应用---技能和个人的两部分网络以及基因产品和基因本体论（GO）术语之间的网络 - 证明了自动从数据中自动提取或增强本体的可能性。
We propose a method for extracting hierarchical backbones from a bipartite network. Our method leverages the observation that a hierarchical relationship between two nodes in a bipartite network is often manifested as an asymmetry in the conditional probability of observing the connections to them from the other node set. Our method estimates both the importance and direction of the hierarchical relationship between a pair of nodes, thereby providing a flexible way to identify the essential part of the networks. Using semi-synthetic benchmarks, we show that our method outperforms existing methods at identifying planted hierarchy while offering more flexibility. Application of our method to empirical datasets---a bipartite network of skills and individuals as well as the network between gene products and Gene Ontology (GO) terms---demonstrates the possibility of automatically extracting or augmenting ontology from data.
