Advertisement

Social-Textual Query Processing on Graph Database Systems

  • Oshini Goonetilleke
  • Timos Sellis
  • Xiuzhen Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10837)

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.

References

  1. 1.
    Armenatzoglou, N., Papadopoulos, S., Papadias, D.: A general framework for geo-social query processing. PVLDB 6(10), 913–924 (2013)Google Scholar
  2. 2.
    Bahmani, B., Goel, A.: Partitioned multi-indexing: bringing order to social search. In: WWW 2012, pp. 399–408. ACM, New York (2012)Google Scholar
  3. 3.
    Busch, M., Gade, K., Larson, B., Lok, P., Luckenbill, S., Lin, J.: Earlybird: real-time search at Twitter. In: ICDE 2012, pp. 1360–1369 (2012)Google Scholar
  4. 4.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1), 337–348 (2009)Google Scholar
  5. 5.
    Curtiss, M., Becker, I., et al.: Unicorn: a system for searching the social graph. PVLDB 6(11), 1150–1161 (2013)Google Scholar
  6. 6.
    Elbassuoni, S., Blanco, R.: Keyword search over RDF graphs. In: CIKM 2011, pp. 237–242. ACM (2011)Google Scholar
  7. 7.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66(4), 614–656 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: XRANK: ranked keyword search over XML documents. In: SIGMOD 2003, pp. 16–27 (2003)Google Scholar
  9. 9.
    He, H., Wang, H., Yang, J., Yu, P.S.: BLINKS: ranked keyword searches on graphs. In: SIGMOD, pp. 305–316 (2007)Google Scholar
  10. 10.
    İnkaya, T.: A parameter-free similarity graph for spectral clustering. Expert Syst. Appl. 42(24), 9489–9498 (2015)CrossRefGoogle Scholar
  11. 11.
    Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distrib. Comput. 48(1), 96–129 (1998)CrossRefGoogle Scholar
  12. 12.
    Li, Y., Bao, Z., Li, G., Tan, K.: Real time personalized search on social networks. In: ICDE, pp. 639–650 (2015)Google Scholar
  13. 13.
    Li, Z., Lee, K.C.K., Zheng, B., Lee, W., Lee, D.L., Wang, X.: IR-tree: an efficient index for geographic document search. TKDE 23(4), 585–599 (2011)Google Scholar
  14. 14.
    Liu, J., Wang, C., Danilevsky, M., Han, J.: Large-scale spectral clustering on graphs. In: IJCAI 2013, pp. 1486–1492. AAAI Press (2013)Google Scholar
  15. 15.
    Mouratidis, K., Li, J., Tang, Y., Mamoulis, N.: Joint search by social and spatial proximity. In: ICDE, pp. 1578–1579 (2016)Google Scholar
  16. 16.
    Neo4j: Neo4j Graph Database (2017). https://neo4j.com/product/
  17. 17.
    Qiao, M., Qin, L., Cheng, H., Yu, J.X., Tian, W.: Top-k nearest keyword search on large graphs. Proc. VLDB Endow. 6(10), 901–912 (2013)CrossRefGoogle Scholar
  18. 18.
    Sun, Z., Wang, H., Wang, H., Shao, B., Li, J.: Efficient subgraph matching on billion node graphs. PVLDB 5(9), 788–799 (2012)Google Scholar
  19. 19.
  20. 20.
    Trißl, S., Leser, U.: Fast and practical indexing and querying of very large graphs. In: SIGMOD, pp. 845–856 (2007)Google Scholar
  21. 21.
    Vieira, M.V., Fonseca, B.M., Damazio, R., Golgher, P.B., de Castro Reis, D., Ribeiro-Neto, B.A.: Efficient search ranking in social networks. In: CIKM, pp. 563–572 (2007)Google Scholar
  22. 22.
    Wang, H., Aggarwal, C.C.: A survey of algorithms for keyword search on graph data. In: Aggarwal, C., Wang, H. (eds.) Managing and Mining Graph Data. Advances in Database Systems, vol. 40, pp. 249–273. Springer, Boston (2010).  https://doi.org/10.1007/978-1-4419-6045-0_8CrossRefzbMATHGoogle Scholar
  23. 23.
    Yang, J., McAuley, J.J., Leskovec, J.: Community detection in networks with node attributes. CoRR abs/1401.7267 (2014)Google Scholar
  24. 24.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural attribute similarities. PVLDB 2(1), 718–729 (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Oshini Goonetilleke
    • 1
  • Timos Sellis
    • 2
  • Xiuzhen Zhang
    • 1
  1. 1.RMIT UniversityMelbourneAustralia
  2. 2.Swinburne University of TechnologyMelbourneAustralia

Personalised recommendations