document: Algorithms for estimating relative importance in networks Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis -- examples include social networks , Web graphs , telecommunication networks , and biological networks . In interactive analysis of such data a natural query is `` which entities are most important in the network relative to a particular individual or set of individuals ? '' We investigate the problem of answering such queries in this paper , focusing in particular on defining and computing the importance of nodes in a graph relative to one or more root nodes . We define a general framework and a number of different algorithms , building on ideas from social networks , graph theory , Markov models , and Web graph analysis . We experimentally evaluate the different properties of these algorithms on toy graphs and demonstrate how our approach can be used to study relative importance in real-world networks including a network of interactions among September 11th terrorists , a network of collaborative research in biotechnology among companies and universities , and a network of co-authorship relationships among computer science researchers .
doc_bio_tags: O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O