Communities recognition in the Chesapeake Bay ecosystem by dynamical clustering algorithms based on different oscillators systems

TL;DR

This paper applies a dynamical clustering algorithm based on oscillator desynchronization to identify marine organism communities in Chesapeake Bay, demonstrating reliable classification and comparison with other methods.

Contribution

It introduces a novel application of dynamical clustering to ecological networks, showing its effectiveness in community detection within marine ecosystems.

Findings

Reliable community classification in Chesapeake Bay ecosystem

Effective use of different oscillator systems for detection

Comparison shows advantages over existing methods

Abstract

We have recently introduced an efficient method for the detection and identification of modules in complex networks, based on the de-synchronization properties (dynamical clustering) of phase oscillators. In this paper we apply the dynamical clustering tecnique to the identification of communities of marine organisms living in the Chesapeake Bay food web. We show that our algorithm is able to perform a very reliable classification of the real communities existing in this ecosystem by using different kinds of dynamical oscillators. We compare also our results with those of other methods for the detection of community structures in complex networks.