Abstract
Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing
deep learning approaches to learn a compact graph
embedding, upon which classic clustering methods like k-means or spectral clustering algorithms
are applied. These two-step frameworks are dif-
ficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the
specific clustering task. In this paper, we propose
a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for
short). Our method focuses on attributed graphs
to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring
nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in
a graph to a compact representation, on which an
inner product decoder is trained to reconstruct the
graph structure. Furthermore, soft labels from the
graph embedding itself are generated to supervise
a self-training graph clustering process, which iteratively refines the clustering results. The selftraining process is jointly learned and optimized
with the graph embedding in a unified framework,
to mutually benefit both components. Experimental
results compared with state-of-the-art algorithms
demonstrate the superiority of our method