Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks

Rao Dong-ning; Wang Jun-xing; Wei lai; Wang Ya-li

doi:10.12052/gdutxb.170146

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Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks

更新时间：2026-05-27

- Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks
- Vol. 35, Issue 2, Pages: 46-50(2018)
- 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
  3. 华南师范大学　经济与管理学院,广东,广州,510631
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170146
  CLC： TP182
- Online First：09 March 2018，
  
  Published：2018
- 稿件说明：
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饶东宁, 王军星, 魏来, 王雅丽. 并行最小割算法及其在金融社交网络中的应用[J]. 广东工业大学学报, 2018, 35(2): 46-50.

Rao Dong-ning, Wang Jun-xing, Wei lai, et al. Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks[J]. 2018, 35(2): 46-50.
饶东宁, 王军星, 魏来, 王雅丽. 并行最小割算法及其在金融社交网络中的应用[J]. 广东工业大学学报, 2018, 35(2): 46-50. DOI： 10.12052/gdutxb.170146.

Rao Dong-ning, Wang Jun-xing, Wei lai, et al. Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks[J]. 2018, 35(2): 46-50. DOI： 10.12052/gdutxb.170146.

摘要

有效实施金融监管已成为金融健康发展的必要保证. 若能够在金融社交网络中

找到一部分承载网络中所有信息流动的关键节点

便能实现整个金融社交网络的有效监管. 金融社交网络图规模通常较大

须开发大规模图处理并行算法. 本文提出基于分布式图处理平台Pregel的并行最小割算法. 实验基于Apache Spark平台开展

所用数据均来自BoardEx数据库. 实验结果表明

在大规模社交网络图的处理中

该算法具有良好性能. 利用该并行算法得到金融社交网络图的最小割

便可有效实施金融监管.

Abstract

Effective financial supervision has become a necessary guarantee for sound development of economy. Supervising the whole financial social network effectively would become possible if a set of key nodes which carry all the information flow in the financial social network can be found. The scale of social network is often quite large

so parallel algorithms for large-scale graph processing are necessary. A Pregel-based parallel algorithm for the minimal cut set problem is proposed. The experiment is conducted on Apache Spark platform. All data used in the experiment is from the BoardEx database. Experiment results show that the algorithm has a good performance in large-scale social network graph processing. With this parallel algorithm

minimal cut sets of financial social network graphs can be obtained so that effective financial supervision can be implemented.

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references

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