For decades, citation counts and impact factor scores have been the primary currency for evaluating scholarly journals. While these measures have the virtue of simplicity, they discard much of the useful information that is inherent in the structure of citation networks. The Eigenfactor algorithm takes into account not only how many citations a journal receives but also where those citations come from. This is a similar approach to how Google ranks web pages, but instead of ranking websites, we rank journals and instead of using hyperlinks, we use citations. This approach to bibliographic data also allows us to map scientific communication over time. This can be a useful tool for placing a journal in the context of the rest of science. We understand, though, that no metric or statistical tool will ever replace reading papers as the best form of evaluation. However, with increasingly limited time and limited budgets of librarians, journal publishers, editors and scholars, there will continue to be a legitimate need for quantitative measures of the scholarly literature. We would like to think that Eigenfactor is a step in the right direction. In this talk, I will give an overview of the Eigenfactor Metrics, some recent developments at Eigenfactor.org and how librarians can use these tools to better inform subscription decisions.