Graph clustering, an important unsupervised problem, has been shown to be
more resistant to advances in Graph Neural Networks (GNNs). In addition, almost
all clustering methods focus on homophilic graphs and ignore heterophily. This
significantly limits their applicability in practice, since real-world graphs
exhibit a structural disparity and cannot simply be classified as homophily and
heterophily. Thus, a principled way to handle practical graphs is urgently
needed. To fill this gap, we provide a novel solution with theoretical support.
Interestingly, we find that most homophilic and heterophilic edges can be
correctly identified on the basis of neighbor information. Motivated by this
finding, we construct two graphs that are highly homophilic and heterophilic,
respectively. They are used to build low-pass and high-pass filters to capture
holistic information. Important features are further enhanced by the
squeeze-and-excitation block. We validate our approach through extensive
experiments on both homophilic and heterophilic graphs. Empirical results
demonstrate the superiority of our method compared to state-of-the-art
clustering methods.

对于图聚类问题，现有的图神经网络方法很难取得进展，同时大多数聚类方法只关注同质图而忽视异质图，限制了它们在实践中的适用性。本文提供了一种具有理论支持的新颖解决方案，通过邻居信息对同质性和异质性边进行识别，并构建高同质性和高异质性的图，用于构建低通滤波器和高通滤波器以捕获综合信息，并通过压缩 - 激励块增强重要特征，实验证明本方法在同质图和异质图上优于现有聚类方法。