  Effectively modelling hidden structures in a network is very practical but
theoretically challenging. Existing relational models only involve very limited
information, namely the binary directional link data, embedded in a network to
learn hidden networking structures. There is other rich and meaningful
information (e.g., various attributes of entities and more granular information
than binary elements such as "like" or "dislike") missed, which play a critical
role in forming and understanding relations in a network. In this work, we
propose an informative relational model (InfRM) framework to adequately involve
rich information and its granularity in a network, including metadata
information about each entity and various forms of link data. Firstly, an
effective metadata information incorporation method is employed on the prior
information from relational models MMSB and LFRM. This is to encourage the
entities with similar metadata information to have similar hidden structures.
Secondly, we propose various solutions to cater for alternative forms of link
data. Substantial efforts have been made towards modelling appropriateness and
efficiency, for example, using conjugate priors. We evaluate our framework and
its inference algorithms in different datasets, which shows the generality and
effectiveness of our models in capturing implicit structures in networks.
