A statistical inference approach to structural reconstruction of complex networks from binary time series
Chuang Ma, Han-Shuang Chen, Ying-Cheng Lai, Hai-Feng Zhang

TL;DR
This paper introduces a statistical inference method using the EM algorithm to accurately reconstruct complex network structures from binary time series data without prior knowledge of dynamics, even with noise.
Contribution
It develops a novel, parameter-free EM-based approach for network reconstruction from binary data, capable of distinguishing links without prior dynamical information.
Findings
Accurately reconstructs network topology from binary data.
Works with various dynamical processes and topologies.
Effective even with noisy data.
Abstract
Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains to be challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum likelihood estimation of the probabilities associated with actual or non-existent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any {\em a priori} knowledge of the detailed…
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