Concept Discovery in Unstructured Data. 2nd International Workshop, CDUD 2012, Leuven, Belgium, May 2012, Proceedings
In this paper we propose two novel methods for analyzing data collected from online social networks. In particular we will do analyses on Vkontake data (Russian online social network). Using biclustering we extract groups of users with similar interests and find communities of users which belong to similar groups. With triclustering we reveal users’ interests as tags and use them to describe Vkontakte groups. After this social tagging process we can recommend to a particular user relevant groups to join or new friends from interesting groups which have a similar taste. We present some preliminary results and explain how we are going to apply these methods on massive data repositories.
The goal of the expert search task is nding knowledgeable persons within the enterprise. In this paper we focus on its distinctions from the other information retrieval tasks. We review the existing approaches and propose a new term weighting scheme which is based on analysis of communication patterns between people. The eectiveness of the proposed approach is evaluated on a collection of e-mails from an organization of approximately 1500 people. Results show that it is possible to take into account communication structure in the process of term weighting, eectively combining communication-based and document-based approaches to expert finding.