Abstract
Graph database systems are increasingly being used to store and query large-scale property graphs with complex relationships. Graph data, particularly the ones generated from social networks generally has text associated to the graph. Although graph systems provide support for efficient graph-based queries, there have not been comprehensive studies on how other dimensions, such as text, stored within a graph can work well together with graph traversals. In this paper we focus on a query that can process graph traversal and text search in combination in a graph database system and rank users measured as a combination of their social distance and the relevance of the text description to the query keyword. Our proposed algorithm leverages graph partitioning techniques to speed-up query processing along both dimensions. We conduct experiments on real-world large graph datasets and show benefits of our algorithm compared to several other baseline schemes.
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Goonetilleke, O., Sellis, T., Zhang, X. (2018). Social-Textual Query Processing on Graph Database Systems. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_6
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DOI: https://doi.org/10.1007/978-3-319-92013-9_6
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