Adaptive Skip-Train Structured Regression for Temporal Networks
Adaptive Skip-Train Structured Regression for Temporal Networks
复制标题
时态网络的自适应 Skip-Train 结构化回归
DOI：
10.1007/978-3-319-71246-8_19
复制
发表时间：
2017
影响因子：
6.6
通讯作者：
Z. Obradovic
中科院分区：
文献类型：
--
作者：
M. Pavlovski;Fang Zhou;Ivan Stojkovic;L. Kocarev;Z. Obradovic
广泛的高影响应用涉及在时间网络环境中学习预测模型。在天气预报、预测治疗效果、医疗保健结果和许多其他领域中，网络通常很大，而连续时间时刻之间的间隔很短。因此，在不影响准确性的情况下，需要模型以更可扩展和更有效的方式进行预测。高斯条件随机场（GCRF）是一种广泛应用于网络结构回归的图形模型。然而，GCRF并不适用于大型网络，它在学习时考虑的是整个网络，无法捕获不同的网络子结构（社区）。在这项研究中，我们提出了一种新的模型，自适应跳列结构化集成（AST-SE），这是一种基于采样的结构化回归集成，用于在时间网络上进行预测。AST-SE利用集成方法方案，允许多个gcrf从多个子网学习。提出的模型能够自动跳过整个训练或训练过程的某些阶段。评估了AST-SE的预测准确性和效率，并与合成时间网络和H3N2病毒流感网络的替代方法进行了比较。得到的结果证明：(1)AST-SE比GCRF快\(\sim \) 140倍，因为它经常跳过再训练；(2)在仅对网络的局部视图进行操作时，它仍然比GCRF更准确地捕获原始网络结构；(3)它优于未加权和加权的GCRF集合，后者也在子网上运行，但每个时间步都需要重新训练。与本章相关的代码和数据可在https://doi.org/10.6084/m9.figshare.5444500上获得。
A broad range of high impact applications involve learning a predictive model in a temporal network environment. In weather forecasting, predicting effectiveness of treatments, outcomes in healthcare and in many other domains, networks are often large, while intervals between consecutive time moments are brief. Therefore, models are required to forecast in a more scalable and efficient way, without compromising accuracy. The Gaussian Conditional Random Field (GCRF) is a widely used graphical model for performing structured regression on networks. However, GCRF is not applicable to large networks and it cannot capture different network substructures (communities) since it considers the entire network while learning. In this study, we present a novel model, Adaptive Skip-Train Structured Ensemble (AST-SE), which is a sampling-based structured regression ensemble for prediction on top of temporal networks. AST-SE takes advantage of the scheme of ensemble methods to allow multiple GCRFs to learn from several subnetworks. The proposed model is able to automatically skip the entire training or some phases of the training process. The prediction accuracy and efficiency of AST-SE were assessed and compared against alternatives on synthetic temporal networks and the H3N2 Virus Influenza network. The obtained results provide evidence that (1) AST-SE is \(\sim \)140 times faster than GCRF as it skips retraining quite frequently; (2) It still captures the original network structure more accurately than GCRF while operating solely on partial views of the network; (3) It outperforms both unweighted and weighted GCRF ensembles which also operate on subnetworks but require retraining at each timestep. Code and data related to this chapter are available at: https://doi.org/10.6084/m9.figshare.5444500.
DOI：
10.1007/3-540-45014-9
发表时间：
2000-06
期刊：
--
影响因子：
--
作者：
Thomas G. Dietterich
通讯作者：
Thomas G. Dietterich
影响因子：
30.3
作者：
Zaas AK;Chen M;Varkey J;Veldman T;Hero AO 3rd;Lucas J;Huang Y;Turner R;Gilbert A;Lambkin-Williams R;Øien NC;Nicholson B;Kingsmore S;Carin L;Woods CW;Ginsburg GS
通讯作者：
Ginsburg GS