基于Hadoop的跨社交网络局部时序链路预测算法

doi:10.13229/j.cnki.jdxbgxb20200798

Abstract

Abstract:

In order to improve the accuracy and stability of local time series link prediction for cross social network， a Hadoop based local time series link prediction algorithm is proposed. This algorithm selects six cross social network node similarity indicators， and designs a parallel computing model using the core component MapReduce of Hadoop， which can segment and process the massive parallel data in cross social network， and reduce the computational complexity. Based on this， the local time series link prediction algorithm based on MapReduce parallel operation model is used to obtain the point-to-point prediction score in the network by using the selected node similarity index， so as to realize the prediction of cross social network local temporal link. The experimental results show that the prediction accuracy of the proposed algorithm is high， it can maintain a stable prediction state， and has good comprehensive prediction performance.

Key words: computer application, cross social networks, Hadoop, link prediction, MapReduce operation model, parallel processing, similarity index

CLC Number:

TP311

Su-ming KANG,Ye-e ZHANG. Hadoop⁃based local timing link prediction algorithm across social networks[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 626-632.

Figures/Tables 6

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实验网络编号	连边数	节点数	聚类系数	网络直径	平均度	平均最短路径
a	2731	187	0.607	5	27.59	2.13
b	2137	286	0.297	4	14.36	2.35
c	6247	2364	0.022	14	9.74	6.34
d	6583	4930	0.101	45	2.56	18.88
e	1495	78	0.285	6	33.57	2.05
f	2115	321	0.738	5	12.70	2.63
g	11682	5011	0.377	14	2.38	4.99
h	1364	131	0.306	7	30.16	2.08

实验网络编号	CN 指标	Slaton 指标	Jaccard 指标	RA 指标	AA 指标	LAS 指标
a	0.931	0.925	0.926	0.948	0.943	0.895
b	0.814	0.826	0.831	0.811	0.819	0.771
c	0.752	0.745	0.749	0.726	0.754	0.772
d	0.833	0.844	0.844	0.841	0.829	0.811
e	0.781	0.787	0.786	0.782	0.787	0.905
f	0.948	0.925	0.913	0.952	0.936	0.903
g	0.937	0.938	0.936	0.937	0.934	0.939
h	0.852	0.886	0.836	0.815	0.871	0.856

实验网络编号	CN 指标	Slaton 指标	Jaccard 指标	RA 指标	AA 指标	LAS 指标
a	0.711	0.719	0.726	0.758	0.734	0.755
b	0.612	0.633	0.598	0.647	0.667	0.681
c	0.599	0.526	0.568	0.587	0.602	0.631
d	0.627	0.634	0.668	0.706	0.654	0.669
e	0.582	0.581	0.567	0.577	0.561	0.556
f	0.787	0.724	0.776	0.792	0.713	0.706
g	0.711	0.752	0.726	0.734	0.762	0.725
h	0.691	0.654	0.668	0.692	0.603	0.611

实验网络编号	CN 指标	Slaton 指标	Jaccard 指标	RA 指标	AA 指标	LAS 指标
a	0.044	0.038	0.046	0.047	0.045	0.033
b	0.052	0.047	0.051	0.044	0.049	0.036
c	0.019	0.025	0.023	0.028	0.026	0.022
d	0.011	0.015	0.009	0.013	0.018	0.016
e	0.046	0.059	0.062	0.036	0.053	0.096
f	0.038	0.045	0.036	0.033	0.037	0.041
g	0.002	0.004	0.003	0.003	0.005	0.006
h	0.033	0.039	0.029	0.036	0.038	0.041

Hadoop⁃based local timing link prediction algorithm across social networks

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