One of the challenges in mining information networks is the lack of intrinsic metric in representing nodes into a low dimensional space, which is essential in many mining tasks, such as recommendation and anomaly detection. Moreover, when coming to heterogeneous information networks, where nodes belong to different types and links represent different semantic meanings, it is even more challenging to represent nodes properly. In this talk, we will focus on two mining tasks, i.e., (1) content-based recommendation and (2) anomaly detection in heterogeneous categorical events, and introduce (1) how to represent nodes when different types of nodes and links are involved; and (2) how heterogeneous links play different roles in these tasks. Our results have demonstrated the superiority as well as the interpretability of these new methodologies.