This paper addresses the issue of extraction of an academic researcher social network. By researcher social network extraction, we are aimed at finding, extracting, and fusing the `semantic'-based profiling information of a researcher from the Web. Previously, social network extraction was often undertaken separately in an ad-hoc fashion. This paper first gives a formalization of the entire problem. Specifically, it identifies the `relevant documents' from the Web by a classifier. It then proposes a unified approach to perform the researcher profiling using Conditional Random Fields (CRF). It integrates publications from the existing bibliography datasets. In the integration, it proposes a constraints-based probabilistic model to name disambiguation. Experimental results on an online system show that the unified approach to researcher profiling significantly outperforms the baseline methods of using rule learning or classification. Experimental results also indicate that our method to name disambiguation performs better than the baseline method using unsupervised learning. The methods have been applied to expert finding. Experiments show that the accuracy of expert finding can be significantly improved by using the proposed methods.