Group extraction from professional social network using a new semi-supervised hierarchical clustering

• Published in: Knowledge and information systems Vol. 40; no. 1; pp. 29 - 47
• Main Authors: Ben Ahmed, Eya, Nabli, Ahlem, Gargouri, Faïez
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2014; Springer; Springer Nature B.V
• Subjects: Analysis; Applied sciences; Clustering; Communities; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Criteria; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Data warehouses; Database Management; Exact sciences and technology; Extraction; Fuses; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Information systems. Data bases; IT in Business; Job openings; Memory organisation. Data processing; Professional relationships; Professionals; Regular Paper; Representations; Social networks; Social research; Software; Studies; User generated content; Web 2.0; Group; Professional social network; Constraint; Professional network warehousing; Group extraction; Social network; User profile; Semi-supervised clustering; Quantitative ranked constraints; Clustering; Data warehouse; Cluster analysis; Group building; Hierarchical classification; Social group; Cluster; Experimental result; Semi-supervised learning; User behavior
• DOI: 10.1007/s10115-013-0634-x
• ISSN: 0219-1377

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.