This article shows how a machine can employ a network view to reason about complex social relations of news reliability. Such a network view promises a topic-agnostic perspective that can be a useful hint on reliability trends and their heterogeneous assumptions. In our analysis, we depart from the ever-growing numbers of papers trying to find machine learning algorithms to predict the reliability of news and focus instead on using machine reasoning to understand the structure of news networks by comparing it with our human judgements. Understanding and representing news networks is not easy, not only because they can be extremely vast but also because they are shaped by several overlapping network dynamics. We present a machine learning approach to analyse what constitutes reliable news from the view of a network. Our aim is to machine-read a network's understanding of news reliability. To analyse real-life news sites, we used the Décodex dataset to train machine learning models from the structure of the underlying network. We then employ the models to draw conclusions how the Décodex evaluators came to assess the reliability of news.