  On one hand, compared with traditional relational and XML models, graphs have
more expressive power and are widely used today. On the other hand, various
applications of social computing trigger the pressing need of a new search
paradigm. In this article, we argue that big graph search is the one filling
this gap. To show this, we first introduce the application of graph search in
various scenarios. We then formalize the graph search problem, and give an
analysis of graph search from an evolutionary point of view, followed by the
evidences from both the industry and academia. After that, we analyze the
difficulties and challenges of big graph search. Finally, we present three
classes of techniques towards big graph search: query techniques, data
techniques and distributed computing techniques.
