Abstract
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content
information, and several recent methods based on
it have achieved promising clustering performance
on some real attributed networks. However, there
is limited understanding of how graph convolution
affects clustering performance and how to properly
use it to optimize performance for different graphs.
Existing methods essentially use graph convolution
of a fixed and low order that only takes into account
neighbours within a few hops of each node, which
underutilizes node relations and ignores the diversity of graphs. In this paper, we propose an adaptive
graph convolution method for attributed graph clustering that exploits high-order graph convolution to
capture global cluster structure and adaptively selects the appropriate order for different graphs. We
establish the validity of our method by theoretical
analysis and extensive experiments on benchmark
datasets. Empirical results show that our method
compares favourably with state-of-the-art methods