Journal on Communications ›› 2023, Vol. 44 ›› Issue (3): 145-156.doi: 10.11959/j.issn.1000-436x.2023060
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Jian SHU1, Jiawei SHI1, Linlan LIU2, Al-Kali Manar1
Revised:
2023-02-27
Online:
2023-03-25
Published:
2023-03-01
Supported by:
CLC Number:
Jian SHU, Jiawei SHI, Linlan LIU, Al-Kali Manar. Topology prediction for opportunistic network based on spatiotemporal convolution[J]. Journal on Communications, 2023, 44(3): 145-156.
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名称 | 类型 | 描述 |
Self-Attention | 序列模型 | 采用注意力机制计算每个时间步的注意力值以提取时序特征 |
CTDNE | 基于随机游走的预测模型 | 通过随机游走方法得到网络嵌入,将该嵌入运用于拓扑预测任务 |
Dyngraph2vec | 基于自编码器的预测模型 | 采用自编码器与RNN得到网络嵌入,将该嵌入运用于拓扑预测任务 |
E-LSTM-D | 基于自编码器的预测模型 | 自编码器和LSTM结合模型 |
GCN-GAN | 基于图卷积网络的预测模型 | GCN与LSTM结合模型,并利用GAN强化模型学习能力 |
SE-GRU | 基于图卷积网络的预测模型 | 结构编码与GRU结合模型,并设计多种损失约束 |
T-GCN | 时空图神经网络 | GCN与GRU结合模型 |
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预测方法 | ITC | MIT | Asturias-er | 平均值 | |||||||
AUC | PRAUC | AUC | PRAUC | AUC | PRAUC | AUC | PRAUC | ||||
移除时空图 | 0.940 8 | 0.853 8 | 0.960 2 | 0.847 6 | 0.968 4 | 0.959 8 | 0.956 4 | 0.887 0 | |||
移除拓扑变化矩阵 | 0.954 6 | 0.883 8 | 0.966 1 | 0.885 3 | 0.985 3 | 0.966 0 | 0.968 6 | 0.911 7 | |||
移除门控单元 | 0.953 1 | 0.880 6 | 0.964 8 | 0.877 9 | 0.986 5 | 0.966 5 | 0.968 1 | 0.908 3 | |||
DTW-STC | 0.963 2 | 0.901 9 | 0.970 5 | 0.905 3 | 0.994 7 | 0.985 0 | 0.976 1 | 0.930 7 |
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预测方法 | ITC | MIT | Asturias-er | |||||
AUC | PRAUC | AUC | PRAUC | AUC | PRAUC | |||
Self-Attention | 0.955 9 | 0.755 5 | 0.962 9 | 0.764 7 | 0.971 8 | 0.858 6 | ||
CTDNE | 0.736 8 | 0.760 7 | 0.827 5 | 0.850 8 | 0.691 6 | 0.767 9 | ||
Dyngraph2vec | 0.956 9 | 0.885 4 | 0.938 1 | 0.842 0 | 0.965 3 | 0.905 5 | ||
E-LSTM-D | 0.945 1 | 0.877 5 | 0.948 5 | 0.850 2 | 0.981 2 | 0.946 7 | ||
GCN-GAN | 0.944 7 | 0.874 2 | 0.970 2 | 0.890 6 | 0.988 3 | 0.952 3 | ||
SE-GRU | 0.954 0 | 0.849 2 | 0.940 2 | 0.851 6 | 0.976 8 | 0.911 5 | ||
T-GCN | 0.912 0 | 0.820 1 | 0.874 3 | 0.812 2 | 0.911 8 | 0.858 2 | ||
DTW-STC | 0.963 2 | 0.901 9 | 0.970 5 | 0.905 3 | 0.994 7 | 0.985 0 |
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