Formal Concept Analysis Applied to Professional Social Networks Analysis

Best Student Paper Award in area of Databases and Information Systems Integration for the paper entitled: Formal Concept Analysis applied to Professional Social Networks Analysis, 19th International Conference on Enterprise Information Systems (ICEIS).

Abstract: From the recent proliferation of online social networks, a set of specific type of social network is attracting more and more interest from people all around the world. It is professional social networks, where the users’ interest is oriented to business. The behavior analysis of this type of user can generate knowledge about competences that people have been developed in their professional career. In this scenario, and considering the available amount of information in professional social networks, it has been fundamental the adoption of effective computational methods to analyze these networks. The formal concept analysis (FCA) has been a effective technique to social network analysis (SNA), because it allows identify conceptual structures in data sets, through conceptual lattice and implication rules. Particularly, a specific set of implications rules, know as proper implications, can represent the minimum set of conditions to reach a specific goal. In this work, we proposed a FCA-based approach to identify relations among professional competences through proper implications. The experimental results, with professional profiles from LinkedIn and proper implications   extracted from PropIm algorithm, shows the minimum sets of skills that is necessary to reach job positions.

Silva, P., Dias, S., Brandão, W., Song, M. and Zárate, L. Formal Concept Analysis Applied to Professional Social Networks Analysis. In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) – Volume 1, pages 123-134. ISBN: 978-989-758-247-9 Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Author: Sérgio M Dias

Sérgio M. Dias is a Data scientist, Professor, and Researcher in Computer Science.

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