Complex Network in Solar Features

TL;DR

This paper reviews the application of complex network theory to solar features, demonstrating its effectiveness in identifying active regions and predicting solar events through network parameters.

Contribution

It provides a comprehensive overview of how complex network analysis can be applied to solar features, including new insights into network parameters for solar activity prediction.

Findings

Active areas on the solar surface are correctly identified.

Network parameters like clustering coefficient and PageRank are useful for event prediction.

Sunspots are formed through complex nonlinear dynamics.

Abstract

This paper is an overview of studying the solar features in a complex network approach. First, we introduce the structural features of complex networks and important network parameters. Applying the detrended fluctuation and rescaled range analysis and nodes degree power-law distributions confirmed the non-randomness of the solar features complex networks. Using the HEALPix pixelization and considering all parts of the solar surface under the same conditions, as well as applying centrality parameters (the nodes with the highest connectivity, closeness, betweenness, and Pagerank) showed that the active areas on the solar surface were correctly identified and were consistent with observations. A review of the complex structure of the solar proton flux and active regions also showed that in these networks, the average clustering coefficient and Page-rank parameters are suitable criteria to use in event prediction methods. The complex network of sunspots has also shown that sunspots and sunspot groups are formed through complex nonlinear dynamics.