An Efficient Parallel Method for Performing Concurrent Operations on Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

This paper presents our approach to optimize the concurrent operations on a large-scale social network. Here, we focus on the directed, unweighted relationships among members in a social network. It can then be illustrated as a directed, unweighted graph. With such a large-scale dynamic social network, we face the problem of having concurrent operations from adding or removing edges dynamically while one may ask to determine the relationship between two members. To solve this challenge, we propose an efficient parallel method based on (i) utilizing an appropriate data structure, (ii) optimizing the updating actions and (iii) improving the performance of query processing by both reducing the searching space and computing in multi-threaded parallel. Our method was validated by the datasets from SigMod Contest 2016 and SNAP DataSet Collections with the good experimental results compared to other solutions.

Keywords

Bi-directional BFS search Concurrent operations on social networks Multi-threaded parallel computing 

References

  1. 1.
    Gong, M., Li, G., Wang, Z., Ma, L., Tian, D.: An efficient shortest path approach for social networks based on community structure. CAAI Trans. Intell. Technol. 1(1), 114–123 (2016)CrossRefGoogle Scholar
  2. 2.
    Du, P.-H., Pham, H.-D., Nguyen, N.-H.: Optimizing the shortest path query on large-scale dynamic directed graph. In: The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 210–216 (2016)Google Scholar
  3. 3.
    Wei, J., Chen, K., Zhou, Y., Zhou, Q., He, J.: Benchmarking of distributed computing engines spark and graphlab for big data analytics. In: International Conference on Big Data Computing Service and Applications, pp. 10–13 (2016)Google Scholar
  4. 4.
    Hallac, D., Leskovec, J., Boyd, S.: Network lasso: clustering and optimization in large graphs. In: ACM SIGKDD International Conference on KDD, pp. 387–396 (2015)Google Scholar
  5. 5.
    U, L.H., Zhao, H.J., Yiu, M.L., Li, Y., Gong, Z.: Towards online shortest path computation. IEEE Trans. Knowl. Data Eng. 26(4), 1012–1025 (2014)Google Scholar
  6. 6.
    Chakaravarthy, V.T., Checconi, F., Petrini, F., Sabharwal, Y.: Scalable single source shortest path algorithms for massively parallel systems. In: IEEE 28th International Parallel and Distributed Processing Symposium, pp. 889–901 (2014)Google Scholar
  7. 7.
    Mondal, J., Deshpande, A.: Managing large dynamic graphs efficiently. In: Proceedings of the ACM SIGMOD 2012, pp. 145–156 (2012)Google Scholar
  8. 8.
    Yahia, S.A., Benedikt, M., Lakshmanan, L., Stoyanovich, J.: Efficient network aware search in collaborative tagging sites. Proc. VLDB Endow. 1(1), 710–721 (2008)CrossRefGoogle Scholar
  9. 9.
    Leiserson, C.E., Schardl, T.B.: A work-efficient parallel breadth-first search algorithm (or how to cope with the nondeterminism of reducers). In: Proceedings of the Twenty-Second Annual ACM Symposium on Parallelism in Algorithms and Architectures, pp. 303–314 (2010)Google Scholar
  10. 10.
    Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: PowerGraph: distributed graph-parallel computation on natural graphs. In: 10th USENIX Symposium on Operating Systems Design and Implementation, pp. 17–30 (2012)Google Scholar
  11. 11.
    Gonzalez, J.E., Xin, R.S., Dave, A., Crankshaw, D., Franklin, M.J., Stoica, I.: GraphX: graph processing in a distributed dataflow framework. In: 11th USENIX Conference on Operating Systems Design and Implementation, pp. 599–613 (2014)Google Scholar
  12. 12.
    Hagberg, A.A., Schult, D.A., Swar, P.J.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference, pp. 11–15 (2008)Google Scholar
  13. 13.
    The ACM SIGMOD Programming Contest 2016: http://dsg.uwaterloo.ca/sigmod16contest/. Accessed 15 May 2017
  14. 14.
  15. 15.
    Stanford Large Network Dataset Collection: https://snap.stanford.edu/data/index.html. Accessed 15 May 2017

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Phuong-Hanh Du
    • 1
  • Hai-Dang Pham
    • 1
  • Ngoc-Hoa Nguyen
    • 1
  1. 1.Department of Information SystemsVNU University of Engineering and TechnologyHanoiVietnam

Personalised recommendations