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The paper describes the application of graph theoretic concepts to the dynamic cross-correlation data obtained from MD simulations of adenine riboswitch, in the absence and presence of adenine. This novel approach combines both community detection algorithms that support edge weights, and cliques. The effect of variations in the values of nearest neighbors (NN) and correlation coefficient threshold (T) in the community detection algorithm have been applied to identify and filter out coincidental correlations between rogue nodes. The results generated for add Adenine riboswitch based on this hybrid approach, successfully identified the correlations within the structural regions of the molecule, providing strong clues regarding the functionality and stability of the RNA molecule in the absence and presence of adenine. Our results also suggested that a prior application of the proposed algorithm (in an automated fashion) to the simulation data of RNA biomolecules, can provide strong leads for hypothesis formulation and subsequent hypothesis-driven manual investigation.