Abstract
Nowadays, geo-based social group activities have become popular because of the availability of geo-location information. In this paper, we propose a novel Geo-Social Team Formation framework using DSCAN, named DSCAN-GSTF, for impromptu activities, aim to find a group of individuals closest to a location where service requires quickly. The group should be socially cohesive for better collaboration and spatially close to minimize the preparation time. To imitate the real-world scenario, the DSCAN-GSTF framework considers various criteria which can provide effective Geo-Social groups, including a required list of skills, the minimum number of each skill, contribution capacity, and the weight of the user’s skills. The existing geo-social models ignore the expertise level of individuals and fail to process a large geo-social network efficiently, which is highly important for an urgent service request. In addition to considering expertise level in our model, we also utilize the DSCAN method to create clusters in parallel machines, which makes the searching process very fast in large networks. Also, we propose a polynomial parametric network flow algorithm to check the skills criteria, which boosts the searching speed of our model. Finally, extensive experiments were conducted on real datasets to determine a competitive solution compared to other existing state-of-the-art methods.
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MahdavyRad, M., Selvarajah, K., Kobti, Z. (2022). DSCAN for Geo-social Team Formation. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_44
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