An investigation of big graph partitioning methods for distribution of graphs in vertex-centric systems

  • Nasrin Mazaheri Soudani
  • Afsaneh FatemiEmail author
  • Mohammadali Nematbakhsh


Relations among data entities in most big data sets can be modeled by a big graph. Implementation and execution of algorithms related to the structure of big graphs is very important in different fields. Because of the inherently high volume of big graphs, their calculations should be performed in a distributed manner. Some distributed systems based on vertex-centric model have been introduced for big graph calculations in recent years. The performance of these systems in terms of run time depends on the partitioning and distribution of the graph. Therefore, the graph partitioning is a major concern in this field. This paper concentrates on big graph partitioning approaches for distribution of graphs in vertex-centric systems. This briefly discusses vertex-centric systems and formulates different models of graph partitioning problem. Then, a review of recent methods of big graph partitioning for these systems is shown. Most recent methods of big graph partitioning for vertex centric systems can be categorized into three classes: (i) stream-based methods that see vertices or edges of the graph in a stream and partition them, (ii) distributed methods that partition vertices or edges in a distributed manner, and (iii) dynamic methods that change partitions during the execution of algorithms to obtain better performance. This study compares the properties of different approaches in each class and briefly reviews methods that are not in these categories. This comparison indicates that The streaming methods are good choices for initial load of the graph in Vertex-centric systems. The distributed and dynamic methods are appropriate for long-running applications.


Graph partitioning Vertex-centric systems Big graphs Distributed computing 



  1. 1.
    Abbas, Z., Kalavri, V., Carbone, P., Vlassov, V.: Streaming graph partitioning: an experimental study. Proc. VLDB Endow. 11(11), 1590–1603 (2018). CrossRefGoogle Scholar
  2. 2.
    Abou-Rjeili, A., Karypis, G.: Multilevel algorithms for partitioning power-law graphs. In: Proceedings 20th IEEE International Parallel Distributed Processing Symposium (2006).
  3. 3.
    Akhremtsev, Y., Sanders, P., Schulz, C.: High-quality shared-memory graph partitioning (2017). arXiv:1710.08231
  4. 4.
    Aslam, S.: Twitter by the numbers: stats, demographics and fun facts (2018).
  5. 5.
    Aydin, K., Bateni, M., Mirrokni, V.: Distributed balanced partitioning via linear embedding. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 387–396. ACM, New York (2016)Google Scholar
  6. 6.
    Balakrishnan, H., Kaashoek, M.F., Karger, D., Morris, R., Stoica, I.: Looking up data in p2p systems. Commun. ACM 46(2), 43–48 (2003). CrossRefGoogle Scholar
  7. 7.
    Bao, N.T., Suzumura, T.: Towards highly scalable pregel-based graph processing platform with x10. In: Proceedings of the 22Nd International Conference on World Wide Web (WWW ’13) Companion, pp. 501–508. ACM, New York (2013).
  8. 8.
    Bollobs, B.: Random Graphs. Springer, New York (1998)Google Scholar
  9. 9.
    Borthakur, D.: HDFS Architecture Guide. Apache Hadoop Project (2008)Google Scholar
  10. 10.
    Bourse, F., Lelarge, M., Vojnovic, M.: Balanced graph edge partition. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’14), pp. 1456–1465. ACM, New York (2014).
  11. 11.
    Bu, Y., Borkar, V., Jia, J., Carey, M.J., Condie, T.: Pregelix: big(ger) graph analytics on a dataflow engine. Proc. VLDB Endow. 8(2), 161–172 (2014). CrossRefGoogle Scholar
  12. 12.
    Bui, T.N., Jones, C.: Finding good approximate vertex and edge partitions is np-hard. Inf. Process. Lett. 42(3), 153–159 (1992). MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Buluç, A., Meyerhenke, H., Safro, I., Sanders, P., Schulz, C.: Recent advances in graph partitioning. In: Kliemann, L., Sanders, P. (eds.) Algorithm Engineering. LNCS, pp. 117–158. Springer, Cham (2016). CrossRefGoogle Scholar
  14. 14.
    Cao, Y., Rao, R.: A streaming graph partitioning approach on imbalance cluster. In: 2016 18th International Conference on Advanced Communication Technology (ICACT), pp. 360–364 (2016).
  15. 15.
    Chen, R., Shi, J., Chen, Y., Chen, H.: Powerlyra: differentiated graph computation and partitioning on skewed graphs. In: Proceedings of the Tenth European Conference on Computer Systems (EuroSys ’15), pp. 1:1–1:15. ACM, New York (2015).
  16. 16.
    Chen, T., Li, B.: A distributed graph partitioning algorithm for processing large graphs. In: 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), pp. 53–59 (2016)Google Scholar
  17. 17.
    Ching, A.: Giraph: production-grade graph processing infrastructure for trillion edge graphs. In: ATPESC, vol. 14 (2014)Google Scholar
  18. 18.
    Ching, A., Edunov, S., Kabiljo, M., Logothetis, D., Muthukrishnan, S.: One trillion edges: graph processing at Facebook-scale. Proc. VLDB Endow. 8(12), 1804–1815 (2015). CrossRefGoogle Scholar
  19. 19.
    Condon, A., Karp, R.M.: Algorithms for graph partitioning on the planted partition model. Random Struct. Algorithms 18(2), 116–140 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Cormen, T.H.: Introduction to algorithms. MIT, Cambridge (2009)zbMATHGoogle Scholar
  21. 21.
    Cui, H., Cipar, J., Ho, Q., Kim, J.K., Lee, S., Kumar, A., Wei, J., Dai, W., Ganger, G.R., Gibbons, P.B., et al.: Exploiting bounded staleness to speed up big data analytics. In: USENIX Annual Technical Conference, pp. 37–48 (2014)Google Scholar
  22. 22.
    Devine, K., Boman, E., Heaphy, R., Hendrickson, B., Vaughan, C.: Zoltan data management services for parallel dynamic applications. Comput. Sci. Eng. 4(2), 90–97 (2002)CrossRefGoogle Scholar
  23. 23.
    Dindokar, R., Simmhan, Y.: Elastic partition placement for non-stationary graph algorithms. In: 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 90–93 (2016).
  24. 24.
    Dong, F., Zhang, J., Luo, J., Shen, D., Jin, J.: Enabling application-aware flexible graph partition mechanism for parallel graph processing systems. Concurr. Comput. Pract. Exp. 29(6), e3849 (2017)., e3849 cpe.3849
  25. 25.
    Donnelly, G.: 75 super-useful Facebook statistics for 2018 (2018).
  26. 26.
    Echbarthi, G., Kheddouci, H.: Fractional greedy and partial restreaming partitioning: New methods for massive graph partitioning. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 25–32 (2014).
  27. 27.
    Echbarthi, G., Kheddouci, H.: Streaming metis partitioning. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 17–24 (2016).
  28. 28.
    Elsner, U.: Graph partitioning—a survey. Technical Report SFB393/97-27 (1997)Google Scholar
  29. 29.
    Fjllstrm, P.O.: Algorithms for Graph Partitioning: A Survey, vol. 3. Linkping University Electronic Press, Linkping (1998)Google Scholar
  30. 30.
    Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: distributed graph-parallel computation on natural graphs. In: Proceedings of 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI), vol. 12, pp. 17–30 (2012)Google Scholar
  31. 31.
    Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103,018 (2010)CrossRefGoogle Scholar
  32. 32.
    Guerrieri, A., Montresor, A.: Distributed edge partitioning for graph processing (2014). arXiv:1403.6270
  33. 33.
    Guerrieri, A., Montresor, A.: DFEP: Distributed Funding-Based Edge Partitioning, Springer, Berlin, pp. 346–358 (2015)Google Scholar
  34. 34.
    Guo, Y., Hong, S., Chafi, H., Iosup, A., Epema, D.: Modeling, analysis, and experimental comparison of streaming graph-partitioning policies. J. Parallel Distrib. Comput. 108, 106–121. Special Issue on Scalable Computing Systems for Big Data Applications (2017)
  35. 35.
    Han, M., Daudjee, K.: Giraph unchained: barrierless asynchronous parallel execution in pregel-like graph processing systems. Proc. VLDB Endow. 8(9), 950–961 (2015). CrossRefGoogle Scholar
  36. 36.
    Han, W., Miao, Y., Li, K., Wu, M., Yang, F., Zhou, L., Prabhakaran, V., Chen, W., Chen, E.: Chronos: A graph engine for temporal graph analysis. In: Proceedings of the Ninth European Conference on Computer Systems (EuroSys ’14), pp. 1:1–1:14. ACM, New York (2014).
  37. 37.
    Hendawi, A.M., Bao, J., Mokbel, M.F.: iRoad: a framework for scalable predictive query processing on road networks. Proc. VLDB Endow. 6(12), 1262–1265 (2013). CrossRefGoogle Scholar
  38. 38.
    Hoque, I., Gupta, I.: Lfgraph: Simple and fast distributed graph analytics. In: Proceedings of the First ACM SIGOPS Conference on Timely Results in Operating Systems (TRIOS ’13), pp. 9:1–9:17. ACM, New York (2013).
  39. 39.
    Hu, K., Zeng, H.J.W.W.G.: Partitioning big graph with respect to arbitrary proportions in a streaming manner. Future Gen. Comput. Syst. 80, 1–11 (2018). CrossRefGoogle Scholar
  40. 40.
    Hwang, C.R.: Simulated annealing: theory and applications. Acta Appl. Math. 12(1), 108–111 (1988). Google Scholar
  41. 41.
    Jain, N., Liao, G., Willke, T.L.: Graphbuilder: Scalable graph etl framework. In: First International Workshop on Graph Data Management Experiences and Systems (GRADES ’13), pp. 4:1–4:6. ACM, New York (2013).
  42. 42.
    Junghanns, M., Kiessling, M., Teichmann, N., Gomez, K., Petermann, A., Rahm, E.: Declarative and distributed graph analytics with gradoop. Proc. VLDB Endow. 11(12), 2006–2009 (2018). CrossRefGoogle Scholar
  43. 43.
    Khayyat, Z., Awara, K., Alonazi, A., Jamjoom, H., Williams, D., Kalnis, P.: Mizan: A system for dynamic load balancing in large-scale graph processing. In: Proceedings of the 8th ACM European Conference on Computer Systems (EuroSys ’13), pp. 169–182. ACM, New York (2013).
  44. 44.
    Kim, G.H., Trimi, S., Chung, J.H.: Big-data applications in the government sector. Commun. ACM 57(3), 78–85 (2014). CrossRefGoogle Scholar
  45. 45.
    Lang, K.: Finding good nearly balanced cuts in power law graphs. Technical Report YRL-2004-036, Yahoo! Research Labs (2004)Google Scholar
  46. 46.
    Lee, K.H., Lee, Y.J., Choi, H., Chung, Y.D., Moon, B.: Parallel data processing with MapReduce: a survey. SIGMOD Rec. 40(4), 11–20 (2012). CrossRefGoogle Scholar
  47. 47.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1(1), 2 (2007). CrossRefGoogle Scholar
  48. 48.
    Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  49. 49.
    Lim, Y., Lee, W.J., Choi, H.J., Kang, U.: MTP: discovering high quality partitions in real world graphs. World Wide Web, pp. 1–24 (2016)Google Scholar
  50. 50.
    Liu, X., Murata, T.: Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Physica A 389(7), 1493–1500 (2010). CrossRefGoogle Scholar
  51. 51.
    Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.M.: Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5(8), 716–727 (2012). CrossRefGoogle Scholar
  52. 52.
    Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: Pregel: A system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD ’10), pp. 135–146. ACM, New York (2010).
  53. 53.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: the next frontier for innovation, competition, and productivity 2011, vol. 5(33), p. 222 (2015).
  54. 54.
    Margo, D., Seltzer, M.: A scalable distributed graph partitioner. Proc. VLDB Endow. 8(12), 1478–1489 (2015). CrossRefGoogle Scholar
  55. 55.
    Martella, C., Logothetis, D., Loukas, A., Siganos, G.: Spinner: Scalable graph partitioning in the cloud (2014). arXiv:1404.3861
  56. 56.
    Mayer, C., Tariq, M.A., Li, C., Rothermel, K.: Graph: heterogeneity-aware graph computation with adaptive partitioning. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), pp. 118–128 (2016).
  57. 57.
    Mayer, C., Mayer, R., Tariq, M.A., Geppert, H., Laich, L., Rieger, L., Rothermel, K.: Adwise: adaptive window-based streaming edge partitioning for high-speed graph processing (2017). arXiv preprint. arXiv:1712.08367
  58. 58.
    McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D., Barton, D.: Big data’s: the management revolution. Harv. Bus. Rev. 90(10), 61–67 (2012)Google Scholar
  59. 59.
    McSherry, F.: Spectral partitioning of random graphs. In: Proceedings of 42nd IEEE Symposium on Foundations of Computer Science, pp. 529–537 (2001)Google Scholar
  60. 60.
    Meyerhenke, H., Sanders, P., Schulz, C.: Parallel graph partitioning for complex networks. IEEE Trans. Parallel Distrib. Syst. PP(99), 1–1 (2017)Google Scholar
  61. 61.
    Mofrad, M.H., Melhem, R., Hammoud, M.: Revolver: vertex-centric graph partitioning using reinforcement learning. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 818–821 (2018).
  62. 62.
    Moreira, O., Popp, M., Schulz, C.: Graph partitioning with acyclicity constraints (2017). arXiv:1704.00705
  63. 63.
    Nirmala, G., Dinakaran, K.: Analysis of protein database for semantic similarity using map reduce; a survey. In: Proceedings of IEEE International Conference on Computer Communication and Systems (ICCCS14), pp. 046–050 (2014).
  64. 64.
    Nishimura, J., Ugander, J.: Restreaming graph partitioning: simple versatile algorithms for advanced balancing. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’13), pp. 1106–1114. ACM, New York (2013).
  65. 65.
    Onizuka, M., Fujimori, T., Shiokawa, H.: Graph partitioning for distributed graph processing. Data Sci. Eng. 2(1), 94–105 (2017). CrossRefGoogle Scholar
  66. 66.
    Petroni, F., Querzoni, L., Daudjee, K., Kamali, S., Iacoboni, G.: HDRF: Stream-based partitioning for power-law graphs. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM ’15), pp. 243–252. ACM, New York (2015).
  67. 67.
    Rahimian, F., Payberah, A.H., Girdzijauskas, S., Jelasity, M., Haridi, S.: JA-BE-JA: A distributed algorithm for balanced graph partitioning. In: 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems, pp. 51–60 (2013)Google Scholar
  68. 68.
    Rahimian, F., Payberah, A.H., Girdzijauskas, S., Haridi S.: Distributed vertex-cut partitioning. In: Distributed Applications and Interoperable Systems. Springer, Heidelberg, pp. 186–200 (2014)Google Scholar
  69. 69.
    Roy, A., Bindschaedler, L., Malicevic, J., Zwaenepoel, W.: Chaos: Scale-out graph processing from secondary storage. In: Proceedings of the 25th Symposium on Operating Systems Principles (SOSP ’15), pp. 410–424. ACM, New York (2015).
  70. 70.
    Sajjad, H.P., Payberah, A.H., Rahimian, F., Vlassov, V., Haridi, S.: Boosting vertex-cut partitioning for streaming graphs. In: 2016 IEEE International Congress on Big Data (BigData Congress), pp. 1–8 (2016).
  71. 71.
    Sala, A., Cao, L., Wilson, C., Zablit, R., Zheng, H., Zhao, B.Y.: Measurement-calibrated graph models for social network experiments. In: Proceedings of the 19th International Conference on World Wide Web (WWW ’10), pp. 861–870. ACM, New York (2010).
  72. 72.
    Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference (IMC ’11), pp. 81–98. ACM, New York (2011).
  73. 73.
    Salihoglu, S., Widom, J.: GPS: a graph processing system. In: Proceedings of the 25th International Conference on Scientific and Statistical Database Management (SSDBM), pp. 22:1–22:12. ACM, New York (2013).
  74. 74.
    Salihoglu, S., Widom, J.: Optimizing graph algorithms on pregel-like systems. Proc. VLDB Endow. 7(7), 577–588 (2014). CrossRefGoogle Scholar
  75. 75.
    Sanders, P., Schulz, C.: Engineering multilevel graph partitioning algorithms. In: Algorithms—ESA 2011. Springer, Berlin, pp. 469–480 (2011)Google Scholar
  76. 76.
    Shang, Z., Yu, J.X.: Catch the wind: Graph workload balancing on cloud. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 553–564 (2013).
  77. 77.
    Shi, Z., Li, J., Guo, P., Li, S., Feng, D., Su, Y.: Partitioning dynamic graph asynchronously with distributed fennel. Future Gen. Comput. Syst. 71, 32–42 (2017). CrossRefGoogle Scholar
  78. 78.
    Slota, G.M., Madduri, K., Rajamanickam, S.: PuLP: scalable multi-objective multi-constraint partitioning for small-world networks. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 481–490 (2014).
  79. 79.
    Stanton, I.: Streaming balanced graph partitioning algorithms for random graphs. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ’14), pp. 1287–1301 (2014).
  80. 80.
    Stanton, I., Kliot, G.: Streaming graph partitioning for large distributed graphs. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’12), pp. 1222–1230. ACM, New York (2012).
  81. 81.
    Sundaram, N., Satish, N., Patwary, M.M.A., Dulloor, S.R., Anderson, M.J., Vadlamudi, S.G., Das, D., Dubey, P.: GraphMat: high performance graph analytics made productive. Proc. VLDB Endow. 8(11), 1214–1225 (2015). CrossRefGoogle Scholar
  82. 82.
    Suri, S., Vassilvitskii, S.: Counting triangles and the curse of the last reducer. In: Proceedings of the 20th International Conference on World Wide Web (WWW ’11), pp. 607–614. ACM, New York (2011).
  83. 83.
    Tatarowicz, A.L., Curino, C., Jones, E.P.C., Madden, S.: Lookup tables: fine-grained partitioning for distributed databases. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 102–113. (2012).
  84. 84.
    Tsourakakis, C.: Streaming graph partitioning in the planted partition model. In: Proceedings of the 2015 ACM on Conference on Online Social Networks (COSN ’15), pp. 27–35. ACM, New York (2015).
  85. 85.
    Tsourakakis, C., Gkantsidis, C., Radunovic, B., Vojnovic, M.: Fennel: streaming graph partitioning for massive scale graphs. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM ’14), pp. 333–342. ACM, New York (2014).
  86. 86.
    Ugander, J., Backstrom, L.: Balanced label propagation for partitioning massive graphs. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM ’13), pp. 507–516. ACM, New York (2013).
  87. 87.
    Valiant, L.G.: A bridging model for parallel computation. Commun. ACM 33(8), 103–111 (1990). CrossRefGoogle Scholar
  88. 88.
    Vaquero, L.M., Cuadrado, F., Logothetis, D., Martella, C.: xDGP: a dynamic graph processing system with adaptive partitioning (2013). arXiv:1309.1049
  89. 89.
    Verma, S., Leslie, L.M., Shin, Y., Gupta, I.: An experimental comparison of partitioning strategies in distributed graph processing. Proc. VLDB Endow. 10(5), 493–504 (2017). CrossRefGoogle Scholar
  90. 90.
    Wang, L., Xiao, Y., Shao, B., Wang, H.: How to partition a billion-node graph. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 568–579 (2014).
  91. 91.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998). CrossRefzbMATHGoogle Scholar
  92. 92.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Boston (2012)Google Scholar
  93. 93.
    Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P., Zhao, B.Y.: User interactions in social networks and their implications. In: Proceedings of the 4th ACM European Conference on Computer Systems (EuroSys ’09), pp. 205–218. ACM, New York (2009).
  94. 94.
    Wu, M., Yang, F., Xue, J., Xiao, W., Miao, Y., Wei, L., Lin, H., Dai, Y., Zhou, L.: GraM: Scaling graph computation to the trillions. In: Proceedings of the Sixth ACM Symposium on Cloud Computing (SoCC ’15), pp. 408–421. ACM, New York (2015).
  95. 95.
    Xiao, W., Xue, J., Miao, Y., Li, Z., Chen, C., Wu, M., Li, W., Zhou, L.: Tux2: Distributed graph computation for machine learning. In: Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17), pp. 669–682 (2017)Google Scholar
  96. 96.
    Xiao-Shu, W., Yao, X., Huan, L.: Cloud computing oriented retrieval technology based on big data. In: 2015 IEEE Seventh International Conference on Measuring Technology and Mechatronics Automation, pp. 275–278 (2015).
  97. 97.
    Xie, C., Li, W.J., Zhang, Z.: S-PowerGraph: streaming graph partitioning for natural graphs by vertex-cut (2015). arXiv:1511.02586
  98. 98.
    Xin, R.S., Gonzalez, J.E., Franklin, M.J., Stoica, I.: GraphX: a resilient distributed graph system on spark. In: First International Workshop on Graph Data Management Experiences and Systems (GRADES ’13), pp. 2:1–2:6. ACM, New York (2013).
  99. 99.
    Xu, N., Chen, L., Cui, B.: LogGP: a log-based dynamic graph partitioning method. Proc. VLDB Endow. 7(14), 1917–1928 (2014)CrossRefGoogle Scholar
  100. 100.
    Xu, N., Cui, B., Chen, L., Huang, Z., Shao, Y.: Heterogeneous environment aware streaming graph partitioning. IEEE Trans. Knowl. Data Eng. 27(6), 1560–1572 (2015)CrossRefGoogle Scholar
  101. 101.
    Yan, D., Cheng, J., Lu, Y., Ng, W.: Effective techniques for message reduction and load balancing in distributed graph computation. In: Proceedings of the 24th International Conference on World Wide Web (WWW ’15), pp. 1307–1317. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2015).
  102. 102.
    Yang, S., Yan, X., Zong, B., Khan, A.: Towards effective partition management for large graphs. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD ’12), pp. 517–528. ACM, New York (2012).
  103. 103.
    Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI’12), pp. 2–2. USENIX Association, Berkeley (2012)Google Scholar
  104. 104.
    Zeng, Z., Wu, B., Wang, H.: A parallel graph partitioning algorithm to speed up the large-scale distributed graph mining. In: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine ’12), pp. 61–68. ACM, New York (2012).
  105. 105.
    Zhao, Y., Yoshigoe, K., Xie, M., Zhou, S., Seker, R., Bian, J.: LightGraph: lighten communication in distributed graph-parallel processing. In: 2014 IEEE International Congress on Big Data, pp. 717–724 (2014).
  106. 106.
    Zheng, A., Labrinidis, A., Chrysanthis, P.K.: Architecture-aware graph repartitioning for data-intensive scientific computing. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 78–85 (2014).
  107. 107.
    Zheng, A., Labrinidis, A., Chrysanthis, P.K.: Planar: Parallel lightweight architecture-aware adaptive graph repartitioning. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 121–132 (2016a).
  108. 108.
    Zheng, A., Labrinidis, A., Pisciuneri, P.H., Chrysanthis, P.K., Givi, P.: Paragon: parallel architecture-aware graph partition refinement algorithm. In: EDBT, pp. 365–376 (2016b)Google Scholar
  109. 109.
    Zheng, A., Labrinidis, A., Faloutsos, C.: Skew-resistant graph partitioning. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 151–154 (2017).
  110. 110.
    Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Tech. Rep., Citeseer (2002)Google Scholar
  111. 111.
    Zhu, X., Chen, W., Zheng, W., Ma, X.: Gemini: A computation-centric distributed graph processing system. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 301–316 (2016)Google Scholar

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Authors and Affiliations

  1. 1.Department of Computer EngineeringUniversity of IsfahanIsfahanIran

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