Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlooked. First, the accumulative error, inflicted by learning with noisy clustering assignments, degrades the effectiveness and robustness of the clustering model. This problem is called Feature Randomness. Second, reconstructing the adjacency matrix sets the model to learn irrelevant similarities for the clustering task. This problem is called Feature Drift. Interestingly, the theoretical relation between the aforementioned problems has not yet been investigated. We study these issues from two aspects: (1) there is a trade-off between Feature Randomness and Feature Drift when clustering and reconstruction are performed at the same level, and (2) the problem of Feature Drift is more pronounced for GAE models, compared with vanilla auto-encoder models, due to the graph convolutional operation and the graph decoding design. Motivated by these findings, we reformulate the GAE-based clustering methodology. Our solution is two-fold. First, we propose a sampling operator $\Xi$ that triggers a protection mechanism against the noisy clustering assignments. Second, we propose an operator $\Upsilon$ that triggers a correction mechanism against Feature Drift by gradually transforming the reconstructed graph into a clustering-oriented one. As principal advantages, our solution grants a considerable improvement in clustering effectiveness and robustness and can be easily tailored to existing GAE models.

翻译：最近的图形群集方法采用“图形自动显示”来进行联合组合和嵌入学习。然而,我们忽略了两个关键问题。首先,通过密集分组任务学习,造成累积错误,降低了组合模式的有效性和稳健性。这个问题被称为“特异性随机性”。第二,重建相邻矩阵为了解集群任务的不相干相似之处建立了模型。这个问题被称为“特性钻孔”。有趣的是,上述问题之间的理论关系尚未调查。我们从两个方面研究这些问题:(1) 当集群和重建在同一级别进行时,特性随机性和特性驱动功能之间出现了一种交易。第一,由于对组合任务进行图形化调整,因此对GAE模型模型来说,特性钻探问题比Vanilla自动编码模型更为明显。根据这些发现,我们重新定义了基于GE的集群方法。我们的解决办法是两面的。首先,我们建议一个抽样操作者 $\Xif 和功能性驱动者 将一个比例的GRODGG 升级的模型变成一个驱动器。