Our world is becoming increasingly interconnected and so is the data collected from it. Developing computational models capable of correctly representing the underlying interrelated structure and the heterogeneous characteristics of the real-world data is essential for representing and reasoning about it. Domains such as biology, online social networks, the World Wide Web, information networks, recommender systems, and scholarly networks are just a few examples that include explicit or implicit interdependent structures.

In this talk, I will present approaches to model heterogeneous interlinked data ranging from feature-based and embedding-based approaches to statistical relational learning methods that more explicitly model the dependencies between entities. I will discuss different methods of modeling node classification and link inference in networks for several domains and highlight the effect of two important aspects: (1) Heterogeneous entities and multi-relational structures, (2) joint inference and collective classification of the unlabeled data. I will also introduce a model for link inference that serves as a template to encode a variety of information such as structural, biological, social, and contextual interactions in various domains.