Abstract: Complex network is a typical representation of complex systems. Community structure is one of the most important structural characteristics of complex network. Aiming at the problem that the current community detection algorithms have low community detection accuracy and is not suitable for large-scale networks, a Deep Auto-encoder and EForest (DA-EF) algorithm and an influence diffusion similarity index are proposed. The DA-EF algorithm combines a multi-layer auto-encoder with a EForest to form a two-level cascade model, transforms the similarity matrix into low dimension and higher order feature matrices through dimensionality reduction and characterization learning, and finally uses K-means to obtain community detection results. The cascade structure greatly reduces the time complexity of the algorithm while maintaining the same depth of the algorithm. The simulation results show that, compared with similar algorithms such as K-means, Spectral and CoDDA, the proposed algorithm has the best NMI and modularity Q values, and the lowest running time of clustering on synthetic datasets and real datasets. It has the advantages of high accuracy and high efficiency. In the performance experiment of the algorithm, the rationality and effectiveness of the cascade structure, the depth of the auto-encoder, and the similarity index of the algorithm are verified.

Key words: complex network, auto-encoder, EForest, community structure, community detection