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

Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network