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The Scientific World Journal
Volume 2014 (2014), Article ID 359868, 13 pages
Research Article

Extraction of Multilayered Social Networks from Activity Data

1School of Natural and Mathematical Sciences, Department of Informatics, King’s College London, London WC2R 2LS, UK
2Institute of Informatics, Wrocław University of Technology, Wyb. Wyspiánskiego 27, 50-370 Wrocław, Poland
3Research & Engineering Center Sp. z o.o., Ulica Strzegomska 46B, 53-611 Wrocław, Poland

Received 23 January 2014; Accepted 6 March 2014; Published 2 July 2014

Academic Editors: H. R. Karimi, Z. Yu, and W. Zhang

Copyright © 2014 Katarzyna Musial et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The data gathered in all kinds of web-based systems, which enable users to interact with each other, provides an opportunity to extract social networks that consist of people and relationships between them. The emerging structures are very complex due to the number and type of discovered connections. In web-based systems, the characteristic element of each interaction between users is that there is always an object that serves as a communication medium. This can be, for example, an e-mail sent from one user to another or post at the forum authored by one user and commented on by others. Based on these objects and activities that users perform towards them, different kinds of relationships can be identified and extracted. Additional challenge arises from the fact that hierarchies can exist between objects; for example, a forum consists of one or more groups of topics, and each of them contains topics that finally include posts. In this paper, we propose a new method for creation of multilayered social network based on the data about users activities towards different types of objects between which the hierarchy exists. Due to the flattening, preprocessing procedure of new layers and new relationships in the multilayered social network can be identified and analysed.