Formal Concept Analysis Applied to Professional Social Networks Analysis

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 tosocial 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 fromPropImalgorithm, shows the minimum sets of skills that is necessary to reach job positions.

[1]  Luis E. Zárate,et al.  Minimal Cover of Implication Rules to Represent Two Mode Networks , 2015, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).

[2]  Lei Li,et al.  Career Trajectory Analysis of Information Technology Alumni: A LinkedIn Perspective , 2016, SIGITE.

[3]  Nick Bassiliades,et al.  Ontology-based sentiment analysis of twitter posts , 2013, Expert Syst. Appl..

[4]  Martin Atzmüller,et al.  Subgroup and Community Analytics on Attributed Graphs , 2015, SNAFCA@ICFCA.

[5]  Karell Bertet,et al.  The multiple facets of the canonical direct unit implicational basis , 2010, Theor. Comput. Sci..

[6]  Stanislav Krajci.  Social Network and Formal Concept Analysis , 2014, Social Networks: A Framework of Computational Intelligence.

[7]  Tomás de Aquino Guimarães,et al.  Gestão de competências e gestao de desempenho: tecnologias distintas ou instrumentos de um mesmo construto? , 2001 .

[8]  Václav Snásel,et al.  Analyzing Social Networks Using FCA: Complexity Aspects , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[9]  Jaume Baixeries i Juvillà,et al.  Contributions to the formalization of order-like dependencies using FCA , 2016 .

[10]  Sérgio M. Dias,et al.  Using implications from FCA to represent a two mode network data , 2015, SEKE.

[11]  Angel Mora,et al.  Knowledge discovery in social networks by using a logic-based treatment of implications , 2015, Knowl. Based Syst..

[12]  Bénédicte Le Grand,et al.  Advances in FCA-based Applications for Social Networks Analysis , 2013, Int. J. Concept. Struct. Smart Appl..

[13]  S. M. Dias.  Redução de reticulados conceituais , 2016 .

[14]  Omar Boussaïd,et al.  An Efficient Method for Community Detection Based on Formal Concept Analysis , 2014, ISMIS.

[15]  Alexey Neznanov,et al.  Analyzing Social Networks Services Using Formal Concept Analysis Research Toolbox , 2015, SNAFCA@ICFCA.

[16]  Henry Soldano,et al.  Abstract and Local Concepts in Attributed Networks , 2015, SNAFCA@ICFCA.

[17]  Martine Collard,et al.  Social-Based Conceptual Links: Conceptual Analysis Applied to Social Networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[18]  Anmol Bhasin,et al.  Modeling professional similarity by mining professional career trajectories , 2014, KDD.

[19]  Yves Bastide,et al.  Computing Proper Implications , 2001 .

[20]  Rokia Missaoui,et al.  CAISL: Simplification Logic for Conditional Attribute Implications , 2016, CLA.

[21]  Luis E. Zárate,et al.  Canonical Computational Models Based on Formal Concept Analysis for Social Network Analysis and Representation , 2015, 2015 IEEE International Conference on Web Services.

[22]  Rostislav Yavorskiy,et al.  Profiling Less Active Users in Online Communities , 2015, SNAFCA@ICFCA.

[23]  Marie-Aude Aufaure,et al.  A Buzz and E-Reputation Monitoring Tool for Twitter Based on Galois Lattices , 2011, ICCS.