在线社交网络个体影响力算法测试与性能评估

全拥; 贾焰; 张良; 朱争; 周斌; 方滨兴

doi:10.11959/j.issn.1000-436x.2018217

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在线社交网络个体影响力算法测试与性能评估

学术论文 | 更新时间：2024-06-05

- 在线社交网络个体影响力算法测试与性能评估
- Performance analysis and testing of personal influence algorithm in online social networks
- 通信学报 2018年39卷第10期页码：1-10
- 作者机构：
  
  1. 国防科技大学计算机学院，湖南长沙 410073
  2. 北京邮电大学计算机学院，北京 100876
- 作者简介：
  
  [ "全拥（1988-），男，湖南常德人，国防科技大学博士生，主要研究方向为在线社交网络分析、数据挖掘。" ]
  [ "贾焰（1960-），女，四川成都人，博士，国防科技大学教授、博士生导师，主要研究方向为数据挖掘、大数据分析、信息安全等。" ]
  [ "张良（1989-），男，江西九江人，国防科技大学博士生，主要研究方向为在线社交网络分析、数据挖掘。" ]
  [ "朱争（1993-），男，四川攀枝花人，国防科技大学硕士生，主要研究方向为信息安全。" ]
  [ "周斌（1971-），男，江西吉安人，博士，国防科技大学研究员、博士生导师，主要研究方向为数据挖掘、信息安全。" ]
  [ "方滨兴（1960-），男，江西上饶人，博士，中国工程院院士，北京邮电大学教授、博士生导师，主要研究方向为计算机网络、信息安全、并行计算等。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB0803303);国家自然科学基金资助项目(61502517);湖南省重点研发计划资助项目(2018GK2056)
- DOI：10.11959/j.issn.1000-436x.2018217
  中图分类号： TP391
- 网络出版日期：2018-10，
  
  纸质出版日期：2018-10-25
- 稿件说明：
移动端阅览
全拥, 贾焰, 张良, 等. 在线社交网络个体影响力算法测试与性能评估[J]. 通信学报, 2018,39(10):1-10.

Yong QUAN, Yan JIA, Liang ZHANG, et al. Performance analysis and testing of personal influence algorithm in online social networks[J]. Journal on communications, 2018, 39(10): 1-10.
全拥, 贾焰, 张良, 等. 在线社交网络个体影响力算法测试与性能评估[J]. 通信学报, 2018,39(10):1-10. DOI： 10.11959/j.issn.1000-436x.2018217.

Yong QUAN, Yan JIA, Liang ZHANG, et al. Performance analysis and testing of personal influence algorithm in online social networks[J]. Journal on communications, 2018, 39(10): 1-10. DOI： 10.11959/j.issn.1000-436x.2018217.

摘要

社交影响力是驱动信息传播的关键因素，基于在线社交网络数据，可以对社交影响力进行建模和分析。针对一种经典的个体影响力计算方法，介绍了该算法的2种并行化实现，并在真实大规模在线社交网络数据集上进行了性能测试。结果表明，借助现有的大数据处理框架，显著提高了个体影响力计算方法在海量数据集中的计算效率，同时也给该类算法的研究和优化提供了实证依据。

Abstract

Social influence is the key factor to drive information propagation in online social networks and can be modeled and analyzed with social networking data.As a kind of classical personal influence algorithm

two parallel implementation versions of a PageRank based method were introduced.Furthermore

extensive experiments were conducted on a large-scale real dataset to test the performance of these parallel methods in a distributed environment.The results demonstrate that the computational efficiency of the personal influence algorithm can be improved significantly in massive data sets by virtue of existing big data processing framework

and provide an empirical reference for the future research and optimization of the algorithm as well.

关键词

Keywords

references

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