【摘要】 针对自动编码器仅对单个数据所包含的内容信息进行特征提取，忽略了数据之间结构信息的问题，提出一种基于异构融合和判别损失的深度图聚类网络.首先，将两个自动编码器获取的异质信息进行融合，解决了采用单一自动编码器提取特征时的信息丢失问题；其次，在聚类训练模块基于类内分布一致性设计判别损失函数，使模型可以端到端地训练，避免了两阶段训练方法中出现特征提取与聚类算法提前假设不匹配的情况；最后，在6个常用数据集上进行实验并验证了该方法的有效性.实验结果表明，与现有的大多数深度图聚类模型相比，该方法在非图数据集和图数据集上的聚类性能有明显提升.更多还原

【Abstract】 Aiming at the problem that autoencoder only extracted features from the content information contained in a single data, ignoring the structure information of data, we proposed a deep graph clustering network based on heterogeneous fusion and discriminant loss. Firstly, the heterogeneous information obtained by two autoencoders was fused, and the problem of information loss was solved when a single autoencoder was used to extract features. Secondly, the discriminant loss function was designed in the clustering training module based on the consistency of distribution within the same cluster, so that the model could be trained end-to-end, and avoiding the mismatch between the feature extraction and the assumptions of the clustering algorithm in the two-stage training methods. Finally, experiments were carried out on six commonly used datasets to verify the effectiveness of the proposed method. The experimental results show that compared with most existing deep graph clustering models, the proposed method significantly improves the clustering performance on both non-graph and graph datasets.更多还原