A particularly helpful search of a network such as the Internet or a citation network not only finds nodes that satisfy some criteria but also ranks those nodes for importance to create what amounts to a "reading list". Traditionally, this ranking was independent of the particular search performed. In part for reasons of speed, the nodes were ranked on the basis of their pre-computed importance within the entire network. More recently, however, relatively rapid algorithms have been developed to permit search-specific rankings. Thus, when these newer methodologies are used, if web pages or nodes in a citation network cover multiple topics and a particular document is important with respect to Topic A and far less important on Topic B, a search for documents based on the presence of Topic B will select this node but will not rank it highly. These newer methodologies essentially create a ranked and topic-specific "reading list" to explore the information revealed by the search. The key to these new methodologies is the creation of a "neighborhood graph", often containing far fewer nodes and edges than the full network to which various algorithms for computing node centrality can be rapidly applied.