The recent advent of high throughput methods has generated large amounts of gene interaction data. This has allowed the construction of genome-wide networks. A significant number of genes in such networks remain uncharacterized and predicting the molecular function of these genes remains a major challenge. A number of existing techniques assume that genes with similar functions are topologically close in the network. Our hypothesis is that genes with similar functions observe similar annotation patterns in their neighborhood, regardless of the distance between them in the interaction network. We thus predict molecular functions of uncharacterized genes by comparing their functional neighborhoods to genes of known function. We propose a two-phase approach. First we extract functional neighborhood features of a gene using Random Walks with Restarts. We then employ a KNN classifier to predict the function of uncharacterized genes based on the computed neighborhood features. We perform leave-one-out validation experiments on two S. cerevisiae interaction networks revealing significant improvements over previous techniques. Our technique also provides a natural control of the trade-off between accuracy and coverage of prediction.