  Attributed graph clustering is one of the most fundamental tasks among graph
learning field, the goal of which is to group nodes with similar
representations into the same cluster without human annotations. Recent studies
based on graph contrastive learning method have achieved remarkable results
when exploit graph-structured data. However, most existing methods 1) do not
directly address the clustering task, since the representation learning and
clustering process are separated; 2) depend too much on data augmentation,
which greatly limits the capability of contrastive learning; 3) ignore the
contrastive message for clustering tasks, which adversely degenerate the
clustering results. In this paper, we propose a Neighborhood Contrast Framework
for Attributed Graph Clustering, namely NCAGC, seeking for conquering the
aforementioned limitations. Specifically, by leveraging the Neighborhood
Contrast Module, the representation of neighbor nodes will be 'push closer' and
become clustering-oriented with the neighborhood contrast loss. Moreover, a
Contrastive Self-Expression Module is built by minimizing the node
representation before and after the self-expression layer to constraint the
learning of self-expression matrix. All the modules of NCAGC are optimized in a
unified framework, so the learned node representation contains
clustering-oriented messages. Extensive experimental results on four attributed
graph datasets demonstrate the promising performance of NCAGC compared with 16
state-of-the-art clustering methods. The code is available at
https://github.com/wangtong627/NCAGC.
