This project was motivated by the need to meaningfully display large amounts of Social Network Analysis (SNA) data from an exploratory case study into the existence of technological convergence in Australia. We found that many tools used for the display of SNA data did not handle large datasets well due to the denseness of information, a typical problem in the display of large graphs. The approach we offer in this paper does not address our motivating problem. Instead of handling large graphs we sought an alternative approach that would allow us to use the tools we had by mining the dataset for interesting concepts and displaying that subset. This is work in progress and we intend to do more work on our graphics tool and exploring alternative algorithms. Our initial results show that a machine learning approach can provide useful information from SNA data that may not have been apparent from typical SNA techniques.
Richards, Debbie and Higgins, Phillip, "Mining Social Network Analysis Data" (2001). ACIS 2001 Proceedings. 79.