Networks have been used to model many real-world phenomena, including biological systems. The recent explosion in biological network data has spurred research in analysis and modeling of these data sets. The expectation is that network data will be as useful as the sequence data in uncovering new biology. The definition of a network (also called a graph) is very simple: it is a set of objects, called nodes, along with pairwise relationships that link the nodes, called links or edges. Biological networks come in many different flavors, depending on the type of biological phenomenon that they model. They can model protein structure: in these networks, called protein structure networks, or residue interaction graphs (RIGs), nodes represent amino acid residues and edges exist between residues that are close in the protein crystal structure, usually within 5 Å (Figure 15.1). Also, they can model protein–protein interactions (PPls): in these networks, proteins are modeled as nodes and edges exist between pairs of nodes corresponding to proteins that can physically bind to each other (Figure 15.2a). Hence, PPI and RIG networks are naturally undirected, meaning that edge AB is the same as edge BA. When all proteins in a cell are considered, these networks are quite large, containing thousands of proteins and tens of thousands of interactions, even for model organisms. An illustration of the PPI network of baker's yeast, Saccharomyces cerevisiae, is presented in Figure 15.2b. Networks can model many other biological phenomena, including transcriptional regulation, functional associations between genes (e.g. synthetic lethality), metabolism, and neuronal synaptic connections.