In our life we are embedded in different networks - our friendships, our relatives, the hierarchies at the workplace or the transport networks we use in space. This networks are intertwined and their characteristics affect each other in different feedback loops. The study of them in isolation is useful, but in order to advance we need higher order structures. Take for example your network of acquaintances. They are probably spread in space, but , in order to form, you have mostly met in the same place with most of your friends. Here we propose a model for the embedding of one network into another, where the largest network is thought as a social network and the other is a spatial network. Using random walks with only one parameter - the stopping probability - we are able to create embeddings of the social network in space that range from very local (all your friends are near) to very delocalized (where your friends could be very far away). 

Flexible model of network embedding

J Fernández-Gracia, JP OnnelaScientific reports9 (1), 1-7