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Adaptive filtering for hidden node detection and tracking in networks.

Franz Hamilton¹, Beverly Setzer¹, Sergio Chavez¹

¹Department of Mathematics, North Carolina State University, Raleigh, North Carolina 27695, USA.

Chaos (Woodbury, N.Y.)

|August 3, 2017

Summary

This summary is machine-generated.

We developed a new adaptive filtering method to detect hidden nodes in network dynamics. This approach helps identify false connections and track hidden node influence over time, crucial for accurate network inference.

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Area of Science:

Neuroscience
Network Science
Signal Processing

Background:

Accurate network connectivity identification from noisy time series is vital for understanding network dynamics.
Hidden nodes in networks act as unobserved drivers, complicating connectivity estimation and leading to false connections.
Detecting the influence of these hidden nodes is critical for correct network inference.

Purpose of the Study:

To propose a novel method for detecting hidden nodes in network dynamics.
To apply this method to neuronal networks for identifying hidden influences.
To enable accurate network inference by accounting for unobserved drivers.

Main Methods:

Utilizing an adaptive filtering framework to model hidden nodes as missing variables.
Employing estimated system noise covariance to localize and differentiate hidden node effects.
Implementing a sequential algorithm for real-time tracking of hidden node influence.

Main Results:

The adaptive filter successfully estimates system noise covariance.
This covariance estimation allows for the localization of hidden node impacts.
The method can distinguish the influence of multiple hidden nodes and track changes over time.

Conclusions:

The proposed adaptive filtering method effectively detects hidden nodes in network dynamics.
This technique improves the accuracy of network inference, particularly in complex systems like neuronal networks.
The algorithm's sequential nature facilitates dynamic tracking of hidden influences.