Analysis of Contractions in System Graphs: Application to State Estimation

TL;DR

This paper investigates how the global clustering coefficient of system graphs influences observability and measurement requirements, revealing that higher GCC can reduce measurements needed and affect measurement substitution in state estimation.

Contribution

The study links the global clustering coefficient of system graphs to measurement requirements and observability, providing empirical insights for improving large-scale network analysis.

Findings

Higher GCC correlates with fewer measurements needed for observability.

Increased GCC reduces options for measurement substitution after failures.

Tuning GCC can enhance observability in large-scale networks.

Abstract

Observability and estimation are closely tied to the system structure, which can be visualized as a system graph--a graph that captures the inter-dependencies within the state variables. For example, in social system graphs such inter-dependencies represent the social interactions of different individuals. It was recently shown that contractions, a key concept from graph theory, in the system graph are critical to system observability, as (at least) one state measurement in every contraction is necessary for observability. Thus, the size and number of contractions are critical in recovering for loss of observability. In this paper, the correlation between the average-size/number of contractions and the global clustering coefficient (GCC) of the system graph is studied. Our empirical results show that estimating systems with high GCC requires fewer measurements, and in case of measurement failure, there are fewer possible options to find substitute measurement that recovers the system's observability. This is significant as by tuning the GCC, we can improve the observability properties of large-scale engineered networks, such as social networks and smart grid.