期刊
KNOWLEDGE-BASED SYSTEMS
卷 239, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2021.107996
关键词
Bayesian network; Probabilistic inference; Network embedding; Random walk
资金
- National Natural Science Foundation of China [U1802271, 61802035, 62002311]
- Science Foundation for Distinguished Young Schol-ars of Yunnan Province, China [2019FJ011]
- China Postdoctoral Science Foundation, China [2020M673310]
- Major Project of Science and Technology of Yunnan Province, China [202002AD080002-1-B]
- Cultivation Project of Donglu Scholar of Yunnan University, China
This paper proposes a Deepwalk-based method for Bayesian network embedding, and approximates probabilistic inferences using the distance among embeddings, providing an efficient approach to multiple probabilistic inferences.
As a classical probabilistic graphic model, Bayesian network (BN) is widely used for representing and inferring dependence relationships with uncertainties. However, multiple probabilistic inferences on BN are quite inefficient, since each probabilistic inference on BN is extremely time-consuming and meanwhile the intermediate results cannot be reused. It is necessary to improve the overall efficiency of multiple probabilistic inferences on the same BN by incorporating an easy-to-calculate inference method and an easy-to-reuse technique for common calculations in multiple inference tasks. In this paper, we first propose a Deepwalk based method for BN embedding, as a specification of general graph embedding, which preserves both the graphical structure and conditional probabilities in BN. We then provide the algorithm to approximate probabilistic inferences via the distance among embeddings. We finally present an efficient approach to multiple probabilistic inferences. Extensive experiments illustrate that our proposed method is effective for BN embedding, and outperforms other state-of-the-art competitors by improving the inference efficiency with several orders of magnitude. (c) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据