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 sub...

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Bibliographic Details
Published in:IEEE transactions on network science and engineering Vol. 11; no. 6; pp. 5892 - 5902
Main Authors: Wan, Jinming, Kataoka, Jun, Sivakumar, Jayanth, Pena, Eric, Che, Yiming, Sayama, Hiroki, Cheng, Changqing
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Bayes methods
Bayesian analysis
Compressed sensing
Connectivity
Design optimization
inverse problem
network inference
Network reconstruction
Network systems
Network topologies
Nodes
Noise measurement
Power grids
Power system dynamics
Sensors
Sequences
sequential node selection
Slabs
spike and slab
Time series analysis
Topology
Topology optimization
ISSN:2327-4697, 2334-329X
Online Access:Get full text
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Summary: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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ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2024.3471852