Prediction and Modeling with Networks

Networks are a useful data structure for encoding relational information between a set of entities and appear in a variety of fields, from biology to social science. The use of principled statistical, computational and mathematical tools is crucial for the understanding of the structural and functional relationships encoded in the network. In this talk we will summarize 3 important areas of network science, including, 1) link prediction, 2) anomaly detection, and 3) community detection. We will discuss the practical concerns for the implementation of the state-of-the-art tools in each of the 3 areas. Finally, we will discuss the computational challenges in handling large networks.