Community Structure Based Shortest Path Finding for Social Networks

  • Yale Chai
  • Chunyao SongEmail author
  • Peng Nie
  • Xiaojie Yuan
  • Yao Ge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11029)


With the rapid expansion of communication data, research about analyzing social networks has become a hotspot. Finding the shortest path (SP) in social networks can help us to investigate the potential social relationships. However, it is an arduous task, especially on large-scale problems. There have been many previous studies on the SP problem, but very few of them considered the peculiarity of social networks. This paper proposed a community structure based method to accelerate answering the SP problem of social networks during online queries. We devise a two-stage strategy to strike a balance between offline pre-computation and online consultations. Our goal is to perform fast and accurate online approximations. Experiments show that our method can instantly return the SP result while satisfying accuracy constraint.


Shortest path Social network Community structure 



This work was supported in part by the National Nature Science Foundation of China under the grants 61702285 and 61772289, the Natural Science Foundation of Tianjin under the grants 17JCQNJC00200, and the Fundamental Research Funds for the Central Universities under the grants 63181317.


  1. 1.
    Chang, L., Li, W.: pSCAN: Fast and exact structural graph clustering. ICDE 29(2), 253–264 (2016)MathSciNetGoogle Scholar
  2. 2.
    Gong, M., Li, G.: An efficient shortest path approach for social networks based on community structure. CAAI 1(1), 114–123 (2016)Google Scholar
  3. 3.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Pohl, I.S.: Bi-directional search. Mach. Intell. 6, 127–140 (1971)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Sommer, C.: Shortest-path queries in static networks. ACM Comput. Surv. 46(4), 1–31 (2014)CrossRefGoogle Scholar
  6. 6.
    Goldberg, A.V., Harrelson, C.: Computing the shortest path: A* search meets graph theory. In: 16th SODA, pp. 156–165 (2005)Google Scholar
  7. 7.
    Akiba, T., Sommer, C.: Shortest-path queries for complex networks: exploiting low tree-width outside the core. In: EDBT, pp. 144–155 (2012)Google Scholar
  8. 8.
    Qiao, M., Cheng, H.: Approximate shortest distance computing: a query-dependent local landmark scheme. In: 28th ICDE, pp. 462–473 (2012)Google Scholar
  9. 9.
    Tretyakov, K.: Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs. In: 20th CIKM, pp. 1785–1794 (2012)Google Scholar
  10. 10.
    Cohen, E., Halperin, E.: Reachability and distance queries via 2-hop labels. SIAM J. Comput. 22, 1338–1355 (2003)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jiang, M.: Hop doubling label indexing for point-to-point distance querying on scale-free networks. PVLDB 7, 1203–1214 (2014)Google Scholar
  12. 12.
    Akiba, T., Iwata, Y.: Fast exact shortest-path distance queries on large networks by pruned landmark labeling. In: SIGMOD, pp. 349–360 (2013)Google Scholar
  13. 13.
    Goldberg, A.V., Kaplan, H.: Reach for A* shortest path algorithms with preprocessing. In: 9th DIMACS Implementation Challenge, vol. 74, pp. 93–139 (2009)Google Scholar
  14. 14.
    Delling, D., Goldberg, A.V., Werneck, R.F.: Hub label compression. In: Bonifaci, V., Demetrescu, C., Marchetti-Spaccamela, A. (eds.) SEA 2013. LNCS, vol. 7933, pp. 18–29. Springer, Heidelberg (2013). Scholar
  15. 15.
    Chechik, S.: Approximate distance oracle with constant query time. arXiv abs/1305.3314 (2013)Google Scholar
  16. 16.
    Chen, W.: A compact routing scheme and approximate distance oracle for power-law graphs. ACM Trans. Algorithms 9, 349–360 (2012)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Potamias, M., Bonchi, F.: Fast shortest path distance estimation in large networks. In: CIKM, pp. 867–876 (2009)Google Scholar
  18. 18.
    Andrea Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80, 016118 (2009)CrossRefGoogle Scholar
  19. 19.
    Xie, J.: SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: ICDMW, pp. 344–349 (2012)Google Scholar
  20. 20.
    Newman, M.E.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  21. 21.
    Fu, A.W.C., Wu, H.: IS-LABEL: an independent-set based labeling scheme for point-to-point distance querying on large graphs. VLDB 6(6), 457–468 (2013)MathSciNetGoogle Scholar
  22. 22.
    Hayashi, T., Akiba, T., Kawarabayashi, K.I.: Fully dynamic shortest-path distance query acceleration on massive networks. In: CIKM, pp. 1533–1542 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yale Chai
    • 1
  • Chunyao Song
    • 1
    Email author
  • Peng Nie
    • 1
  • Xiaojie Yuan
    • 1
  • Yao Ge
    • 1
  1. 1.College of Computer and Control EngineeringNankai UniversityTianjinPeople’s Republic of China

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