Using small-world network topology to refine experimentally-derived networks
Since their formalization in 1998 by Watts and Strogatz, small-world networks have inspired a plethora of new research directions. Many real networks have since been shown to have the small-world network properties of cohesive neighborhoods (high clustering coefficient) and short average distances between vertices (short characteristic path length). Some of these are experimentally determined and susceptible to errors, yet important inferences are still drawn from them. After an overview of the concept of small-world networks and some experimentally-determined networks in biology, I will focus on a small-world network derived from high throughput (and error-prone) experiments to discern interactions between proteins. We exploit the neighborhood cohesiveness property of small-world networks to assess confidence for individual protein-protein interactions. By ascertaining how well each protein-protein interaction (edge) fits the pattern of a small-world network, we stratify even those edges with identical experimental evidence. While much analysis has been done on small-world networks, small-world properties have not previously been used to improve our understanding of individual edges in experimentally-derived graphs. This is joint work with Frederick P. Roth.