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- Title
Group extraction from professional social network using a new semi-supervised hierarchical clustering.
- Authors
Ben Ahmed, Eya; Nabli, Ahlem; Gargouri, Faïez
- Abstract
Recently, social network has been given much attention. This paper addresses the issue of extraction groups from professional social network and enriches the representation of the user profile and its related groups through building a social network warehousing. Several criteria may be applied to detect groups within professional communities, such as the area of expertise, the job openings proposed by the group, the security of the group, and the time of the group creation. In this paper, we aim to find, extract, and fuse the LinkedIn users. Indeed, we deal with the group extraction of LinkedIn users based on their profiles using our innovative semi-supervised clustering method based on quantitative constraints ranking. The encouraging experimental results carried out on our real professional warehouse show the usefulness of our approach.
- Subjects
SOCIAL networks; DATA warehousing; LINKEDIN (Web resource); ONLINE social networks; FACEBOOK (Web resource)
- Publication
Knowledge & Information Systems, 2014, Vol 40, Issue 1, p29
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-013-0634-x