Abstract: Aiming at the quality and efficiency problems caused by insufficient utilization of multilayer relations in large-scale group decision-making, we proposed  a group consensus decision-making method based on multilayer network structure detection. The method  modelled multilayer relations of decision-makers as distinguishable coupled organizations, developed  an interactive restart random walk algorithm to map opinion propagation, detected community structure and influence distribution, and optimized detection efficiency by using nearest neighbor approximation incremental computing. The calculation  examples and simulation results verify that it can strengthen the leading role of decision-makers with strong influence, reduce conflicts, achieve the balance between high consensus and efficient computing, and provide an effective approach for group decision-making in complex coupled environments.

Key words:  , group decision-making, social network analysis, multilayer network framework, structure detection