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摘要: 时序链路预测是动态网络分析的重要组成部分,具有极大的理论和应用价值. 传统的时序链路预测方法往往直接对边的演化规律进行分析,忽略了网络中其他微观结构的演化对链路形成的影响. 基于此分析,本文引入非负张量分解和时间序列分析对网络模体的演化规律进行研究,进而提出一种基于模体演化的链路预测方法. 在三个真实数据集上的实验结果表明,该方法能有效提高链路预测精度.Abstract: Temporal link prediction, which has great value in both research and application, is an important component of the dynamic network analysis. Traditional temporal link prediction methods trend to directly analyze the edge's evolution regularity, while ignoring the influence of other microstructure evolution on link formation. On the basis of this analysis, we adopt the nonnegative tensor decomposition and time series analysis model to extract the evolution regulars of motif, and then propose a temporal link prediction method based on motif evolution. Experimental results on three real data sets show that our method can effectively improve the link prediction accuracy.
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图 6 Facebook数据集不同三元组类型间转换概率随时间变化色谱图
Fig. 6 Chromatogram of triad transition probabilities that change with time in Facebook network
图 7 Facebook数据集中部分随时间变化的三元组转换概率示意图
Fig. 7 Diagram of some triad transition probabilities that change with time in Facebook network
图 10 Facebook数据集TCM算法和TTM算法性能对比图
Fig. 10 Performance contrastgures of TCM algorithm and TTM algorithm in Facebook network
图 11 Enron数据集TCM算法和TTM算法性能对比图
Fig. 11 Performance contrastgures of TCM algorithm and TTM algorithm in Enron network
图 12 Condmat数据集TCM算法和TTM算法性能对比图
Fig. 12 Performance contrastgures of TCM algorithm and TTM algorithm in Condmat network
图 13 时间窗口及三元组重要性指标对TCM算法的影响
Fig. 13 Influence of time window and the triad importance index to TCM method
表 1 实验数据集参数表
Table 1 Parameters of experimental data sets
Condmat Enron Facebook 节点数 17636 22 477 60 290 总边数 88 036 164 081 838 090 快招数 6 16 52 
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表 2 Facebook数据集中各算法预测精度表
Table 2 Accuracy of different methods in Facebook network
PA HPLP TTM TS TCM AUC 0.52 0.76 0.79 0.83 0.84 AUPR 0.03 0.06 0.08 0.1 0.13
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表 3 Enron数据集中各算法预测精度表
Table 3 Accuracy of different methods in Enron network
PA HPLP TTM TS TCM AUC 0.8 0.91 0.81 0.92 0.89 AUPR 0.04 0.17 0.21 0.23 0.30
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表 4 Condmat数据集中各算法预测精度表
Table 4 Accuracy of different methods in Condmat network
PA HPLP TTM TS TCM AUC 0.59 0.68 0.76 0.81 0.92 AUPR 0.24 0.25 0.22 0.25 0.35
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