Abstract
Directionality is a significant property of social networks, which enables us to improve our analytical tasks and have a deeper understanding about social networks. Unfortunately, the potential directionality is hidden in undirected social networks. The previous studies on recovering directionality in undirected social networks mostly focus on the microscopic patterns discovered in the existing directed social networks. In this paper, we attempt to recover the directionality based on the macroscopic community structure. To this end, a variant of the existing modularity model, called behavioural modularity, is designed for discovering community membership of nodes. Assuming that members in the same community have higher behavioural similarity, we introduce the concept of the intra-community popularity, and then estimate directionality of undirected ties based on the community structure and the intra-community popularity. Accordingly, we propose a novel Community and Popularity based Direction Recovering (CPDR) approach to recover the directionality of undirected social networks. Experimental results conducted on three real-world social networks have confirmed the effectiveness of the proposed approach on direction recovery.
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Notes
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The Python code and the benchmark datasets are available at https://www.dropbox.com/sh/dhvhosmzxko0jk2/AACMzPhrm0XdrYZSHI6db_Cda?dl=0 (Extracting Password: DASFAA2018).
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Acknowledgments
This work was supported by NSFC (61502543) and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).
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Wen, YM., Wang, CD., Lin, KY. (2018). Direction Recovery in Undirected Social Networks Based on Community Structure and Popularity. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_34
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DOI: https://doi.org/10.1007/978-3-319-91452-7_34
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