在线社交网络个体影响力算法测试与性能评估

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

Abstract

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.

Key words: performance testing, social influence, distributed computing, online social networks

CLC Number:

TP391

Yong QUAN,Yan JIA,Liang ZHANG,Zheng ZHU,Bin ZHOU,Binxing FANG. Performance analysis and testing of personal influence algorithm in online social networks[J]. Journal on Communications, 2018, 39(10): 1-10.

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References 22

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数据集描述	统计量
博文数	4 586 584 659
用户数	116 147 966
原创博文数	1 079 801 756
被转发原创博文数	97 351 945
转发博文数	3 506 782 903
原创博文平均被转发次数	3.25

数据集	用户数	转发关系数	稠密度
D₁	100 000	4 185 326	4.19×10^-4
D₁_A	100 000	41 862 541	4.19×10^-3
D₁_B	100 000	418 568	4.19×10^-5
D₂	1 000 000	94 091 124	9.41×10^-5
D₂_A	1 000 000	940 634 687	9.41×10^-4
D₂_B	1 000 000	9 411 885	9.41×10^-6
D₂_C	1 000 000	941 482	9.41×10^-7
D₃	10 000 000	662 538 337	6.63×10^-6
D₄	50 000 000	1 368 256 085	5.47×10^-7
D₅	115 205 577	3 504 379 868	1.19×10^-4

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

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