通过稀疏贝叶斯学习从少量数据中对网络连接性进行序列推理

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-03 DOI:10.1109/TNSE.2024.3471852
Jinming Wan;Jun Kataoka;Jayanth Sivakumar;Eric Peña;Yiming Che;Hiroki Sayama;Changqing Cheng
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引用次数: 0

摘要

虽然人们在复杂网络的设计、控制和优化方面做出了巨大努力,但大多数现有工作都假定网络结构是已知的或随时可用的。然而,网络拓扑结构在受到对抗性攻击后可能会被彻底重塑,在后续分析中可能仍然是未知的。在这项工作中,我们提出了一种新颖的贝叶斯序列学习方法,用于自适应地重建网络连通性:所有边的连通性都有一个稀疏的 Spike 和 Slab 先验,从重建节点中学习到的连通性将用于选择下一个节点并更新先验知识。我们方法的核心是,大多数现实网络都是稀疏的,即每个节点的连接度与网络中节点的数量相比要小得多。通过节点间的预期改进实现了对信息量最大的节点的顺序选择。我们在一个合成的最后通牒游戏网络和 IEEE-118 电网系统中证实了这种连通性恢复的顺序贝叶斯方法。结果表明,只需查询部分节点(∼50%)即可揭示网络拓扑结构。
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Sparse Bayesian Learning for Sequential Inference of Network Connectivity From Small Data
While significant efforts have been attempted in the design, control, and optimization of complex networks, most existing works assume the network structure is known or readily available. However, the network topology can be radically recast after an adversarial attack and may remain unknown for subsequent analysis. In this work, we propose a novel Bayesian sequential learning approach to reconstruct network connectivity adaptively: A sparse Spike and Slab prior is placed on connectivity for all edges, and the connectivity learned from reconstructed nodes will be used to select the next node and update the prior knowledge. Central to our approach is that most realistic networks are sparse, in that the connectivity degree of each node is much smaller compared to the number of nodes in the network. Sequential selection of the most informative nodes is realized via the between-node expected improvement. We corroborate this sequential Bayesian approach in connectivity recovery for a synthetic ultimatum game network and the IEEE-118 power grid system. Results indicate that only a fraction (∼50%) of the nodes need to be interrogated to reveal the network topology.
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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