Here we introduce the 'interaction generality' measure, a new method for computationally assessing the reliability of protein-protein interactions obtained in biological experiments. This measure is basically the number of proteins involved in a given interaction and also adopts the idea that interactions observed in a complicated interaction network are likely to be true positives. Using a group of yeast protein-protein interactions identified in various biological experiments, we show that interactions with low generalities are more likely to be reproducible in other independent assays. We constructed more reliable networks by eliminating interactions whose generalities were above a particular threshold. The rate of interactions with common cellular roles increased from 63% in the unadjusted estimates to 79% in the refined networks. As a result, the rate of cross-talk between proteins with different cellular roles decreased, enabling very clear predictions of the functions of some unknown proteins. The results suggest that the interaction generality measure will make interaction data more useful in all organisms and may yield insights into the biological roles of the proteins studied.