Contrastive deep graph clustering (CDGC) utilizes contrastive learning to
group nodes into different clusters. Better augmentation techniques benefit the
quality of the contrastive samples, thus being one of key factors to improve
performance. However, the augmentation samples in existing methods are always
predefined by human experiences, and agnostic from the downstream task
clustering, thus leading to high human resource costs and poor performance. To
this end, we propose an Attribute Graph Clustering method via Learnable
Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for
high-quality and suitable augmented samples for CDGC. Specifically, we design
two learnable augmentors for attribute and structure information, respectively.
Besides, two refinement matrices, including the high-confidence pseudo-label
matrix and the cross-view sample similarity matrix, are generated to improve
the reliability of the learned affinity matrix. During the training procedure,
we notice that there exist differences between the optimization goals for
training learnable augmentors and contrastive learning networks. In other
words, we should both guarantee the consistency of the embeddings as well as
the diversity of the augmented samples. Thus, an adversarial learning mechanism
is designed in our method. Moreover, a two-stage training strategy is leveraged
for the high-confidence refinement matrices. Extensive experimental results
demonstrate the effectiveness of AGCLA on six benchmark datasets