An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observability

Abstract

Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news, brain connectivity networks detection, etc. Many real-world situations suffer from a critical problem that only a limited part of observed nodes are available. This letter considers the problem of network topology inference under the framework of partial observability. Based on the vector autoregressive model, we propose a novel unbiased estimator for the symmetric network topology with the Gaussian noise and the Laplacian combination rule. Theoretically, we prove that it converges to the network combination matrix in probability. Furthermore, by utilizing the Gaussian mixture model algorithm, an effective algorithm called network inference Gauss algorithm is developed to infer the network structure. Finally, compared with the state-of-the-art methods, numerical experiments demonstrate the proposed algorithm enjoys better performance in the case of small sample sizes.

arXiv comment9 pages, 4 figures

References in corpus (6)

• Learning graphs from data: A signal representation perspective
• Connecting the Dots: Identifying Network Structure via Graph Signal Processing
• Local Tomography of Large Networks under the Low-Observability Regime
• Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks
• An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observability
• Graph Learning over Partially Observed Diffusion Networks: Role of Degree Concentration

Cited by in corpus (2)

• An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observability
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