Assessing experimentally derived interactions in a small world

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Abstract
Experimentally determined networks are susceptible to errors, yet important inferences can still be drawn from them. Many real networks have also been shown to have the small-world network properties of cohesive neighborhoods and short average distances between vertices. Although 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. Here we focus on a small-world network derived from high-throughput (and error-prone) protein–protein interaction experiments. 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. This result promises to improve the quality of inference from protein–protein interaction networks in particular and small-world networks in general.