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李贺 等: 基于顶点组重分配的动态增量图划分算法 1839
[19] Tsourakakis C, Gkantsidis C, Radunovic B, Vojnovic M. FENNEL: Streaming graph partitioning for massive scale graphs. In: Proc. of
the 7th ACM Int’l Conf. on Web Search and Data Mining. New York: ACM, 2014. 333–342. [doi: 10.1145/2556195.2556213]
[20] Zhang W, Chen Y, Dai D. AKIN: A streaming graph partitioning algorithm for distributed graph storage systems. In: Proc. of the 18th
IEEE/ACM Int’l Symp. on Cluster, Cloud and Grid Computing. Washington: IEEE, 2018. 183–192. [doi: 10.1109/CCGRID.2018.00033]
[21] LI Q, LI HX, Zhong J, Ying CT, LI Q. Research on graph partitioning in heterogeneous computing environment. Chinese Journal of
Computers, 2021, 44(8): 1751–1766 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2021.01751]
[22] Firth H, Missier P, Aiston J. Loom: Query-aware partitioning of online graphs. In: Proc. of the 21st Int’l Conf. on Extending Database
Technology. Vienna: EDBT, 2018. 337–348.
[23] Schloegel K, Karypis G, Kumar V. Dynamic repartitioning of adaptively refined meshes. In: Proc. of the 1998 ACM/IEEE Conf. on
Supercomputing. Orlando: IEEE, 1998. 29. [doi: 10.1109/SC.1998.10025]
[24] Vaquero LM, Cuadrado F, Logothetis D, Martella C. Adaptive partitioning for large-scale dynamic graphs. In: Proc. of the 34th Int’l
Conf. on Distributed Computing Systems. Madrid: IEEE, 2014. 144–153. [doi: 10.1109/ICDCS.2014.23]
[25] Nicoara D, Kamali S, Daudjee K, Chen L. Hermes: Dynamic partitioning for distributed social network graph databases. In: Proc. of the
18th Int’l Conf. on Extending Database Technology. Brussels: EDBT, 2015. 25–36.
[26] Li H, Yuan H, Huang JB, Cui JT, Yoo J. Dynamic graph repartitioning: From single vertex to vertex group. In: Proc. of the 25th Int’l
Conf. on Database Systems for Advanced Applications. Jeju: Springer, 2020. 482–497. [doi: 10.1007/978-3-030-59416-9_29]
[27] Ou CW, Ranka S. Parallel incremental graph partitioning using linear programming. In: Proc. of the 1994 ACM/IEEE Conf. on
Supercomputing. Washington: IEEE, 1994. 458–467. [doi: 10.1109/SUPERC.1994.344309]
[28] Fan WF, Liu MY, Tian C, Xu RQ, Zhou JR. Incrementalization of graph partitioning algorithms. Proc. of the VLDB Endowment, 2020,
13(8): 1261–1274. [doi: 10.14778/3389133.3389142]
[29] Huang JW, Abadi DJ. Leopard: Lightweight edge-oriented partitioning and replication for dynamic graphs. Proc. of the VLDB
Endowment, 2016, 9(7): 540–551. [doi: 10.14778/2904483.2904486]
[30] Dai D, Zhang W, Chen Y. IOGP: An incremental online graph partitioning algorithm for distributed graph databases. In: Proc. of the 26th
Int ’l Symp. on High-performance Parallel and Distributed Computing. Washington: ACM, 2017. 219 –230. [doi: 10.1145/3078597.
3078606]
[31] Rahimian F, Payberah AH, Girdzijauskas S, Jelasity M, Haridi S. JA-BE-JA: A distributed algorithm for balanced graph partitioning. In:
Proc. of the 7th IEEE Int’l Conf. on Self-adaptive and Self-organizing Systems. Philadelphia: IEEE, 2013. 51–60. [doi: 10.1109/SASO.
2013.13]
[32] Wang L, Xiao YH, Shao B, Wang HX. How to partition a billion-node graph. In: Proc. of the 30th IEEE Int’l Conf. on Data Engineering.
Chicago: IEEE, 2014. 568–579. [doi: 10.1109/ICDE.2014.6816682]
[33] Zheng AG, Labrinidis A, Chrysanthis PK. Planar: Parallel lightweight architecture-aware adaptive graph repartitioning. In: Proc. of the
32nd IEEE Int’l Conf. on Data Engineering. Helsinki: IEEE, 2016. 121–132. [doi: 10.1109/ICDE.2016.7498234]
[34] Shang ZC, Yu JX. Catch the wind: Graph workload balancing on cloud. In: Proc. of the 29th IEEE Int’l Conf. on Data Engineering.
Brisbane: IEEE, 2013. 553–564. [doi: 10.1109/ICDE.2013.6544855]
[35] Xu N, Chen L, Cui B. LogGP: A log-based dynamic graph partitioning method. Proc. of the VLDB Endowment, 2014, 7(14): 1917–1928.
[doi: 10.14778/2733085.2733097]
[36] Predari M, Esnard A. A k-way greedy graph partitioning with initial fixed vertices for parallel applications. In: Proc. of the 24th
Euromicro Int’l Conf. on Parallel, Distributed, and Network-based Processing. Heraklion: IEEE, 2016. 280–287. [doi: 10.1109/PDP.2016.
109]
[37] Abdolrashidi A, Ramaswamy L. Continual and cost-effective partitioning of dynamic graphs for optimizing big graph processing systems.
In: Proc. of the 2016 IEEE Int’l Congress on Big Data. San Francisco: IEEE, 2016. 18–25. [doi: 10.1109/BigDataCongress.2016.12]
[38] Pang S, Chen CJ, Wei T. A realtime community detection algorithm: Incremental label propagation. In: Proc. of the 1st Int’l Conf. on
Future Information Networks. Beijing: IEEE, 2009. 313–317. [doi: 10.1109/ICFIN.2009.5339592]
[39] Raghavan NU, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale networks. Physical Review
E, 2007, 76(3): 036106. [doi: 10.1103/PhysRevE.76.036106]
[40] Stanford large network dataset collection. 2014. http://snap.stanford.edu/data/index.html
[41] Rossi RA, Ahmed NK. The network data repository with interactive graph analytics and visualization. In: Proc. of the 29th AAAI Conf.
on Artificial Intelligence. Austin: AAAI Press, 2015. 4292–4293. [doi: 10.1609/aaai.v29i1.9277]