Graph clustering algorithms with autoencoder structures have recently gained
popularity due to their efficient performance and low training cost. However,
for existing graph autoencoder clustering algorithms based on GCN or GAT, not
only do they lack good generalization ability, but also the number of clusters
clustered by such autoencoder models is difficult to determine automatically.
To solve this problem, we propose a new framework called Graph Clustering with
Masked Autoencoders (GCMA). It employs our designed fusion autoencoder based on
the graph masking method for the fusion coding of graph. It introduces our
improved density-based clustering algorithm as a second decoder while decoding
with multi-target reconstruction. By decoding the mask embedding, our model can
capture more generalized and comprehensive knowledge. The number of clusters
and clustering results can be output end-to-end while improving the
generalization ability. As a nonparametric class method, extensive experiments
demonstrate the superiority of \textit{GCMA} over state-of-the-art baselines.

图聚类算法中的自编码器结构因其高效性能和低训练成本而近来受到关注。然而，对于基于 GCN 或 GAT 的现有图自编码器聚类算法来说，它们不仅缺乏良好的泛化能力，而且很难自动确定由这些自编码器模型聚类的群集数目。为解决这个问题，我们提出了一个称为 Graph Clustering with Masked Autoencoders (GCMA) 的新框架。它利用我们设计的基于图遮蔽方法的融合自编码器来进行图的融合编码。在解码过程中，它引入了我们改进的基于密度的聚类算法作为第二个解码器，并通过多目标重构进行解码。通过解码遮蔽嵌入，我们的模型能够捕捉到更广义和全面的知识。在提高泛化能力的同时，可以输出端到端的群集数目和聚类结果。作为一种非参数类方法，广泛的实验证明了 GCMA 相对于最先进的基准算法的优越性。