Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph
  Clustering
Zichen Wen Yawen Ling Yazhou Ren Tianyi Wu Jianpeng Chen Xiaorong Pu Zhifeng Hao Lifang He
Recently there is a growing focus on graph data, and multi-view graph
clustering has become a popular area of research interest. Most of the existing
methods are only applicable to homophilous graphs, yet the extensive real-world
graph data can hardly fulfill the homophily assumption, where the connected
nodes tend to belong to the same class. Several studies have pointed out that
the poor performance on heterophilous graphs is actually due to the fact that
conventional graph neural networks (GNNs), which are essentially low-pass
filters, discard information other than the low-frequency information on the
graph. Nevertheless, on certain graphs, particularly heterophilous ones,
neglecting high-frequency information and focusing solely on low-frequency
information impedes the learning of node representations. To break this
limitation, our motivation is to perform graph filtering that is closely
related to the homophily degree of the given graph, with the aim of fully
leveraging both low-frequency and high-frequency signals to learn
distinguishable node embedding. In this work, we propose Adaptive Hybrid Graph
Filter for Multi-View Graph Clustering (AHGFC). Specifically, a graph joint
process and graph joint aggregation matrix are first designed by using the
intrinsic node features and adjacency relationship, which makes the low and
high-frequency signals on the graph more distinguishable. Then we design an
adaptive hybrid graph filter that is related to the homophily degree, which
learns the node embedding based on the graph joint aggregation matrix. After
that, the node embedding of each view is weighted and fused into a consensus
embedding for the downstream task. Experimental results show that our proposed
model performs well on six datasets containing homophilous and heterophilous
graphs.