Multi-objective Community Detection Algorithm based on the Adaptive Mutation Operator

Multi-objective optimization algorithms have been applied to community detection in recent years, notwithstanding, there are still problems such as poor stability and low computational efficiency. In order to improve the accuracy and calculation efficiency of community delineation, this paper proposed a multi-objective optimization algorithm (PDMOGA). PDMOGA fuses individual similarity to design a new mutation strategy and adds a de-duplication step to improve the quality of the Pareto frontier. Experimental results show that the algorithm improves stability and accuracy of community delineation compared with GA-NET, MOGA-NET and MOEA/D-NET.