Network and linked data have become quite prevalent in recent years because of the ubiquity of the web and social media applications, which are inherently network oriented. Such networks are massive, dynamic, contain a lot of content, and may evolve over time in terms of the underlying structure. In this paper, we will study the problem of dynamic link inference in temporal and heterogeneous information networks. The problem of dynamic link inference is extremely challenging in massive and heterogeneous information network because of the challenges associated with the dynamic nature of the network, and the different types of nodes and attributes in it. Both the topology and type information need to be used effectively for the link inference process. We propose an effective two-level scheme which makes efficient macro-and micro-decisions for combining structure and content in a dynamic and time-sensitive way. The time-sensitive nature of the links is leveraged in order to perform effective link prediction. We illustrate the effectiveness of our technique over a number of real data sets.
Aggarwal et al. REF tackle the link prediction problem in both dynamic and heterogeneous information networks using a dynamic clustering approach alongside content-based and structural models.
On Dynamic Link Inference in Heterogeneous Networks
SDM
Computer Science