Effective community search over location-based social networks: Conceptual framework with preliminary result

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Alaqta, I
Wang, J
Awrangjeb, M
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Over the past decade, the volume of data has grown exponentially due to global internet service propagation. The number of individuals using the internet has expanded, especially with the use of social networks. Utilising GPS-enabled mobile devices, social networks have been labelled Location-based Social Networks (LBSN). This service enables users to share their current spatial information by ‘checking-in’ with their friends at different locations. This article proposes a conceptual framework to enhance the effectiveness of community search over LBSN. As users are more likely to look for people whom they share similar personalities and interests, these keywords plus the spatial information could help a lot in finding the most appropriate query-based social community. As a result, this paper aims to contribute to the existing body of knowledge as well as the industry in the field of community search (CS). In particular, this work is focusing on CS in the environment of LBSN to benefit from factors of spatial, keywords and time in order to enhance community search models by these factors. Therefore, in this study, we focus on the current state-of-the art of CS and the limitations of integrated models. The preliminary results confirm that user’s checkins can present an alternative approach to produce and update the users’ interests with which we use to boast effectiveness of attributed community search along with spatial information.

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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© Springer Nature Switzerland AG 2019. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
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Alaqta, I; Wang, J; Awrangjeb, M, Effective community search over location-based social networks: Conceptual framework with preliminary result, Lecture Notes in Computer Science, 2019, 11393, pp. 119-131