Networks represent a type of dataset that is ubiquitous in many disciplines and areas. Examples are social networks (ties between people), communication networks, trophic networks ("who eats who"), the World Wide Web, computer networks, lexical networks (connections between words), transport networks, metabolic networks (e.g., interactions between proteins), neural networks, animal networks, citation networks, affiliation networks (of people in groups), software dependency networks, and many more. In this talk, we present ongoing work on answering the question "Can the type of network be detected from the network structure alone?" For instance, given a completely unlabeled network dataset consisting only of node and edges, can we detect whether the data represents a social network or a hyperlink network? We present machine learning and statistical approaches to answering questions of this type. The presented results will make use of data in the KONECT project, one of the largest repositories of network datasets, curated at the University of Namur.