Tutorial: Network Analysis and Modeling
Network science is an active and growing cross-disciplinary area that focuses on the description, representation, analysis, and modeling of complex social, biological, and technological systems as networks or graphs. At its simplest, a network is a collection of nodes, some of which are connected by edges.
For example, nodes might be scientists, and there would be an edge connecting two scientists if they co-authored a scientific paper. Or nodes might be facebook users, and there would be an edge between two users if they were facebook friends. Structures and systems modeled as networks are ubiquitous in the world around us: communication networks, networks of friends and acquaintances (online and in-person), gene regulatory networks, supply chains, and food webs, to name just a few.
In general, networks are used as models in situations in which the architecture of connectivity matters, but where that connectivity is neither random (as in an ideal gas or non-interacting agents) nor regular (as in a crystal lattice or a situation where agents interact spatially). Capturing, modeling, and understanding networks requires understanding both the mathematics of networks and the computational tools for identifying and explaining the patterns they contain.
This course will consist of a survey of techniques for modeling and analyzing the structure and dynamics of networks. We will begin with basic definitions and simple descriptive statistics. We will then look at random graphs, which are useful for generating intuition and serving as simple null models. We will then consider network prediction models that can be used to predict node attributes and missing edges, and approaches for detecting community structure. As time permits, we will examine dynamical models on networks, such as disease and rumor spreading. Throughout, we will take a computational approach, learning how to work with network data and how to implement algorithms for network models and data analysis.
Evaluation will be based on participation in class sessions, coding exercises, and a final project.