Performance analysis and testing of personal influence algorithm in online social networks

Yong QUAN; Yan JIA; Liang ZHANG; Zheng ZHU; Bin ZHOU; Binxing FANG

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

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Performance analysis and testing of personal influence algorithm in online social networks

Papers | 更新时间：2024-06-05

- Performance analysis and testing of personal influence algorithm in online social networks
- Journal on Communications Vol. 39, Issue 10, Pages: 1-10(2018)
- 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：
- 基金信息：
  
  The National Key Research and Development Program of China(2017YFB0803303);The National Natural Science Foundation of China(61502517);The Key Research and Development Project of Hunan Province(2018GK2056)
- DOI：10.11959/j.issn.1000-436x.2018217
  CLC： TP391
- Online First：2018-10，
  
  Published：25 October 2018
- 稿件说明：
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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：

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.

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references

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