<i>n</i>-度中心度与<i>k</i>-压力中心度及其并行算法

饶东宁; 林卓毅; 魏来

doi:10.12052/gdutxb.190156

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n-度中心度与k-压力中心度及其并行算法

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- n-度中心度与k-压力中心度及其并行算法
- n-Degree and k-Stress Centrality with Parallel Algorithms
- 广东工业大学学报 2020年37卷第3期页码：36-41
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 香港大学经济与金融学院,香港,中国,999077
- 作者简介：
  
  饶东宁(1977–)，男，副教授，博士，主要研究方向为智能规划、金融智能
  林卓毅(1992–)，男，硕士研究生，主要研究方向为金融智能，E-mail：13570113148@163.com
- 基金信息：
  
  广东省自然科学基金资助项目(2016A030313700，2016A030313084)
- DOI：10.12052/gdutxb.190156
  中图分类号： TP182
- 网络首发：2020-05-12，
  
  纸质出版：2020
- 稿件说明：
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饶东宁, 林卓毅, 魏来. n-度中心度与k-压力中心度及其并行算法[J]. 广东工业大学学报, 2020, 37(3): 36-41.

Rao Dong-ning, Lin Zhuo-yi, Wei Lai. n-Degree and k-Stress Centrality with Parallel Algorithms[J]. 2020, 37(3): 36-41. DOI: 10.12052/gdutxb.190156.
饶东宁, 林卓毅, 魏来. n-度中心度与k-压力中心度及其并行算法[J]. 广东工业大学学报, 2020, 37(3): 36-41. DOI： 10.12052/gdutxb.190156.

Rao Dong-ning, Lin Zhuo-yi, Wei Lai. n-Degree and k-Stress Centrality with Parallel Algorithms[J]. 2020, 37(3): 36-41. DOI: 10.12052/gdutxb.190156. DOI：

摘要

网络中心度是网络分析的重要指标。文章提出

-度中心度和

-压力中心度以补充网络中心度的研究

此外

并行算法能提高大规模网络的计算效率。为此

在设计网络中心度并行工具包的同时

设计并实现

-度中心度和

-压力中心度的并行算法。工具包的设计基于Spark框架的Pregel方法

并通过BoardEx社交网络测试性能。实验证明工具包的可行性与可拓展性。

Abstract

Network centrality is important for network analysis. Therefore

-Degree and

-Stress centrality are proposed to enrich the analysis options using network centrality. On the other hand

for large scale networks

parallel algorithms are necessary for fast computation. To this end

parallel algorithms for the

-Degree and

-Stress centrality are designed and implemented along with 11 existing centralities in a toolkit. The toolkit is based on Spark Pregel

and tested on the BoardEx social network. Experiments show the feasibility and scalability of the toolkit.

关键词

Keywords

references

FREEMAN L C. Centrality in social networks conceptual clarification [J]. Social Networks, 1978, 1(3): 215-239.

WU X D, DONG B B, DU X Z, et al . Data governance technology [J ] . Journal of Software, 2019, 30(9): 2830-2856.

CHEN L, WU X H. Design of distributed cluster big data dynamic storage system based on Hadoop [J]. Journal of China Academy of Electronics and Information Technology, 2019, 14(6): 593-598.

LIAO W J, HUANG Y F, BAO C K. Memory optimization of Spark parallel computing framework [J]. Computer Engineering Science, 2018, 40(4): 587-593.

SINGH R, CHAKRABORTY A, MANOJ B S. GFT centrality: a new node importance measure for complex networks [J]. Physica A: Statistical Mechanics and its Applications, 2017, 487: 185-195.

MEDEIROS D S V, CAMPISTA M E M, MITTON N, et al . The power of quasi-shortest paths: ρ -geodesic betweenness centrality [J ] . IEEE Transactions on Network Science and Engineering, 2017, 4(3): 187-200.

RAO D N, WEN Y L, WEI L, et al . A weighted centrality algorithm for social networks based on Spark platform in different cultural environments [J ] . Journal of Guangdong University of Technology, 2017, 34(3): 15-20.

WANG L, GUO Q, LIU J G. Measuring node importance based on weighted nonlinear method [J]. Application Research of Computers, 2018, 35(5): 1426-1428.

QIAO S J, GUO J, HAN N, et al . Parallel agorithm for discovering communities in large-scale complex networks [J ] . Chinese Journal of Computers, 2017, 40(3): 687-700.

MA T, YUE M, QU J, et al . PSPLPA: probability and similarity based parallel label propagation algorithm on spark [J ] . Physica A: Statistical Mechanics and its Applications, 2018, 503: 366-378.

AI C, CHEN B, LIU L, et al . Design and implementation of large-scale network propagation simulation method inspired by Pregel mechanism [J ] . Sci Sin Inform, 2018, 48(7): 932-946.

Borgatti S P, Everett M G. A graph-theoretic perspective on centrality [J]. Social Networks, 2006, 28(4): 466-484.

SHIMBEL A. Structural parameters of communication networks [J]. The Bulletin of Mathematical Biophysics, 1953, 15(4): 501-507.

MALEWICZ G, AUSTERN M H, BIK A J C, et al. Pregel: a system for large-scale graph processing[C]//Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. Indianapolis: ACM, 2010: 135-146.

ZAHARIA M, CHOWDHURY M, FRANKLIN M J, et al . Spark: cluster computing with working sets [J ] . HotCloud, 2010, 10(10): 95.

BENZI J, DAMODARAN M. Parallel three dimensional direct simulation Monte Carlo for simulating micro flows [J]. Lect Notes Comput Sci Eng, 2009, 67: 91-98.

RAO D N, WANG J X, WEI L, et al . Parallel minimal cut set algorithm and its application in financial social networks [J ] . Journal of Guangdong University of Technology, 2018, 35(2): 46-50.

廖志军. 分布式K-Core算法加速求解最大团问题及在金融社交网络中应用[D]. 广州: 暨南大学, 2018.

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n-度中心度与k-压力中心度及其并行算法

n-Degree and k-Stress Centrality with Parallel Algorithms

DOI：10.12052/gdutxb.190156

摘要

Abstract

关键词

Keywords

references