Mining of diverse social entities from linked data
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Date
2014
Authors
Cuzzocrea, Alfredo
Leung, Carson K.
Tanbeer, Syed K.
Journal Title
Journal ISSN
Volume Title
Publisher
CEUR Workshop Proceedings
Abstract
Nowadays, high volumes of valuable data can be easily generated or collected from various data sources at high velocity. As these data are often related or linked, they form a web of linked data. Examples include semantic web and social web. The social web captures social relationships that link people (i.e., social entities) through the World Wide Web. Due to the popularity of social networking sites, more people have joined and more online social interactions have taken place. With a huge number of social entities (e.g., users or friends in social networks), it becomes important to analyze high volumes of linked data and discover those diverse social entities. In this paper, we present (i) a tree-based mining algorithm called DF-growth, along with (ii) its related data structure called DF-tree, which allow users to effectively and efficiently mine diverse friends from social networks. Results of our experimental evaluation showed both the time and space-efficiency of our scalable DF-growth algorithm, which makes good use of the DF-tree structure.
Description
A. Cuzzocrea, C.K. Leung, & S.K. Tanbeer. Mining of diverse social entities from linked data. In Proc. EDBT/ICDT Workshops 2014, pp. 269-274. This paper is published in the Workshop Proceedings of the EDBT/ICDT 2014 Joint Conference (March 28, 2014, Athens, Greece) on CEUR-WS.org (ISSN 1613-0073) under the terms of the Creative Commons license CC-by-nc-nd 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0).
Keywords
data mining, friendship patterns, diverse friends, linked data, social networks, extending database technology, database theory
Citation
A. Cuzzocrea, C.K. Leung, & S.K. Tanbeer. Mining of diverse social entities from linked data. In Proc. EDBT/ICDT Workshops 2014, pp. 269-274.