Overlapping Communities Recognition in Socio-Semantic Networks
The aim of this thesis is to investigate and implement one or more efficient algorithms for the study of overlapping communities, which can be applied in information systems such as socio-semantic networks
Communities are an important part of many real-world environments and indicate the formation of special groups, clusters, etc. which are linked by certain relations. The presence and evolution of communities in social networks are an important part in Web 2.0 research. Although the detection of disjoint communities is quite well documented, progress can still be made in the overlapping communities’ area. Moreover, studying the structure and the properties of certain communities across socio-semantic networks may yield interesting results.
The goals of the current thesis are to research the impact of overlapping communities across socio-semantic networks and to develop and evaluate an information system which can analyze data from well-known networks such as blogs, mailing-lists, social networks, etc.. A proper visualization of the results is also part of the assignment.
For more information related to the present thesis announcement, please contact Petru Nicolaescu, M.Sc. .