Prioritization, or ranking, of gene lists is becoming increasingly important for analyzing data generated from high-throughput assays like expression profiling and RNAi-based screening. This is especially the case when specific genes in a list need to be further validated using low-throughput experiments. In addition to gene set overlap enrichment methods, a complementary approach is to examine molecular interaction networks. These can provide putative functional insights based on gene connectivity, especially when many genes contain little or no annotation. For bench and computational biologists alike, using networks requires an informed selection of interaction data for network construction and strategies for managing network complexity. Moreover, graph theory and social network analysis methods can be used to isolate critical subnetworks and quantify network properties. Here, I discuss the basic components of networks, implications of their structure for functional interpretation, and common metrics used for prioritization. Although this is still an ongoing area of research, networks are providing new ways for gauging pathway impact in large-scale data sets.