Looking for sources: Graph search in social media
Facebook graph search is a relatively new technology pioneered/announced by Facebook that attempts to search for relevant info based on social connections in a social media system (eg Facebook). ie it finds relevant info based on exploring information &quot;nearby&quot; on the &quot;social graph&quot;.
in this way, basically every person on Facebook doing the same search with same search terms might come up with different results based on how it interrelates with what their friends are posting about. this also can be seen as part of a trend where the major online services are encroaching on each others prior territory, eg Google's recent introduction of Google+ social network. conversely Facebook is now encroaching on Google's lucrative and highly-developed search engine technology.
there is a huge amount of media coverage [cited in the wikipedia article] but mainly at a superficial level of a product introduction. in contrast to Googles pagerank algorithm, the Facebook algorithm did not appear to originate in open scientific research, but instead is a mostly proprietary technology developed within Facebook. Facebook is not going to want to release many details, but the following is useful for an overview, written by Xiao Li, an engineering manager on the natural language team for Graph Search, and Maxime Boucher a research scientist for Graph Search.

Under the Hood: The natural language interface of Graph Search

also it will be natural for Facebook to insinuate this algorithm as highly developed and competitive with Pagerank but because its in early stages of development, one would not expect highly advanced results competitive with Pagerank at least at first (because Pagerank has been highly developed over a decade etc) with respect to mere semantic capability. on the other hand Facebook has access to a social graph, and it would be expected to prove superior quality in some types of searches that best leverage/focus on ones personal social network.
there does not appear to be much open scientific research outside of Facebook related to this topic. however the following seems to be a publication of some of the principles in a previous prototype/experimental iteration/version called Unicorn which the new feature is probably originating from/growing out of.

Unicorn: a system for searching the social graph

Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes. Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook's Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook's search offerings.