  Socio-technical systems usually consists of many intertwined networks, each
connecting different types of objects (or actors) through a variety of means.
As these networks are co-dependent, one can take advantage of this entangled
structure to study interaction patterns in a particular network from the
information provided by other related networks. A method is hence proposed and
tested to recover the weights of missing or unobserved links in heterogeneous
information networks (HIN) - abstract representations of systems composed of
multiple types of entities and their relations. Given a pair of nodes in a HIN,
this work aims at recovering the exact weight of the incident link to these two
nodes, knowing some other links present in the HIN. To do so, probability
distributions resulting from path-constrained random walks i.e., random walks
where the walker is forced to follow only a specific sequence of node types and
edge types, capable to capture specific semantics and commonly called a
meta-path, are combined in a linearly fashion in order to approximate the
desired result. This method is general enough to compute the link weight
between any types of nodes. Experiments on Twitter and bibliographic data show
the applicability of the method.
