Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network
This work addresses the limitation of existing relational models that use only binary link data, potentially improving network analysis for domains like social networks, but it is incremental as it builds on prior models like MMSB and LFRM.
The authors tackled the problem of learning hidden structures in networks by proposing an informative relational model (InfRM) that incorporates rich metadata and various link data forms, showing generality and effectiveness across different datasets.
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.