Studying natural phenomena via the complex network approach makes it possible to quantify the time-evolving structures with too many elements and achieve a deeper understanding of interactions among the components of a system. In this sense, solar flare as a complex system with the chaotic behavior could be better characterized by the network parameters. Here, we employed an unsupervised network-based method to recognize the position and occurrence time of the solar flares by using the ultraviolet emission (1600 Å) recorded by the Atmospheric Imaging Assembly on board Solar Dynamics Observatory. Three different regions, the flaring active regions, the non-flaring active regions, and the quiet-Sun regions, were considered to study the variations of the network parameters in the presence and absence of flaring phases in various datasets over time intervals of several hours. The whole parts of the selected datasets were partitioned into sub-windows to construct networks based on computing the Pearson correlation between time series of the region of interest and intensities. Analyzing the network parameters such as the clustering coefficient, degree centrality, characteristic length, and PageRank verified that flare triggering has an influence on the network parameters around the flare occurrence time and close to the location of flaring. It was found that the values of the clustering coefficient and characteristic length approach those obtained for the corresponding random network in the flaring phase. These findings could be used for detecting the occurrence times and locations of the region at ultraviolet images.
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