Abstract: The multi-obJective optimization algorithm is competitive in the discovery of complex network communities.However,it is difficult to obtain the satisfied results while dealing with the problem of fuzzy community structure and large scale of network data.To overcome the shortcomings of existing multi-obJective methods,a multi-obJective complex network community discovery algorithm based on spectral clustering is proposed.The proposed algorithm uses spectral clustering to perform initial po-pulation partitioning on the encoded complex network,and exploits its subgraph clustering characteristics to obtain a better initial population.A data reduction method based on grid reduction is applied to reduce the population in the process of evolution,which effectively reduces the complexity of the algorithm.The experimental results on the simulation network and the real network show that the proposed algorithm outperforms than that of the other three representative multi-target based community discovery algorithms in terms of community discovery performance and computational complexity.

Key words: Community discovery, Complex network, Large-scale network, Multi-obJective evolutionary