Effects of hidden nodes on network structure inference

Abstract

Effects of hidden nodes on inference quality of observed network structure are explored based on a disordered Ising model with hidden nodes. We first study analytically small systems consisting of a few nodes, and find that the magnitude of the effective coupling grows as the coupling strength from the hidden common input nodes increases, while the field strength of the input node has opposite effects. Insights gained from analytic results of small systems are confirmed in numerical simulations of large systems. We also find that the inference quality deteriorates as the number of hidden nodes increases. Furthermore, increasing field variance of hidden nodes improves the inference quality of the effective couplings, but worsens the quality for the effective fields. In addition, attenuated coupling strengths involved in at least one hidden node lead to high quality of coupling inference.

16 pages, 10 figures, to appear in J. Phys. A: Math. Theor

References in corpus (3)

• The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity
• Large Pseudo-Counts and $L_2$-Norm Penalties Are Necessary for the Mean-Field Inference of Ising and Potts Models
• Inferring hidden states in a random kinetic Ising model: replica analysis

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