  Social network analysis is leveraged in a variety of applications such as
identifying influential entities, detecting communities with special interests,
and determining the flow of information and innovations. However, existing
approaches for extracting social networks from unstructured Web content do not
scale well and are only feasible for small graphs. In this paper, we introduce
novel methodologies for query-based search engine mining, enabling efficient
extraction of social networks from large amounts of Web data. To this end, we
use patterns in phrase queries for retrieving entity connections, and employ a
bootstrapping approach for iteratively expanding the pattern set. Our
experimental evaluation in different domains demonstrates that our algorithms
provide high quality results and allow for scalable and efficient construction
of social graphs.
