基于深度编码器的复杂网络社区发现算法 #br#

复杂网络是复杂系统的典型表现形式,社区结构是复杂网络最重要的结构特征之一。针对目前社区发现算法精确度低以及不适合大规模网络的问题，提出一种新的算法DA-EF和用于度量节点之间相似度的影响力扩散指标。DA-EF利用多层自动编码器与森林编码器构成二级级联模型，相似度矩阵进行降维和表征学习处理，转化成低维高阶特征矩阵，最终使用K-means得到准确的社区划分结果。级联结构在保持算法同等深度的情况下，大幅降低了算法时间复杂度。在人工合成数据集和真实数据集上的实验表明，DA-EF与同类算法K-means、DA-EML和CoDDA相比，其标准互信息NMI和模块度Q值高，而且聚类运行时间最少，具有精确度高和效率快的优势。在算法性能实验中，验证了算法的级联结构、自动编码器的深度以及影响力扩散指标的合理性和有效性。

Complex network community detectionalgorithm based on deep encoder

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.