The Community Detection of Complex Networks Based on Markov Matrix Spectrum Optimization

This paper presents a novel community detection algorithm for complex networks based on Markov matrix spectrum optimization. An edge cutting model is used to select the edges to be cut by maximizing the second largest eigenvalue of Markov matrix. This model adopts a greedy strategy to ensure that an appropriate number of edges are cut at each iteration of the algorithm, which makes it applicable to large-scale networks. The experimental results on the simulated and real complex networks show that our algorithm can reduce the time complexity of traditional algorithms while maintaining the same performance.