DS 420Network Analytics
Networks constitute a useful representational abstraction for data from many real-world applications, such as social networks, biomolecular networks, brain networks, among others. This course aims to cover the conceptual, algorithmic, and applied aspects of network analytics to prepare the students to analyze network data. Specific topics to be covered include different kinds, e.g., undirected, directed, weighted, unweighted, labeled, unlabeled, networks; network properties and network statistics; statistical models of static and dynamic networks; classes of networks, different classes of network structure, e.g., random, small-world, modular hierarchical; methods for community detection, information propagation, node and link classification, network embedding, and their applications in life sciences e.g., predicting protein function, social sciences, e.g., analyzing social ties, e-commerce e.g., recommender systems.