Abstract
As a fundamental machine learning problem, graph
clustering has facilitated various real-world applications, and tremendous efforts had been devoted to
it in the past few decades. However, most of the existing methods like spectral clustering suffer from
the sparsity, scalability, robustness and handling
high dimensional raw information in clustering. To
address this issue, we propose a deep probabilistic model, called Variational Graph Embedding and
Clustering with Laplacian Eigenmaps (VGECLE),
which learns node embeddings and assigns node
clusters simultaneously. It represents each node
as a Gaussian distribution to disentangle the true
embedding position and the uncertainty from the
graph. With a Mixture of Gaussian (MoG) prior,
VGECLE is capable of learning an interpretable
clustering by the variational inference and generative process. In order to learn the pairwise relationships better, we propose a Teacher-Student mechanism encouraging node to learn a better Gaussian
from its instant neighbors in the stochastic gradient
descent (SGD) training fashion. By optimizing the
graph embedding and the graph clustering problem
as a whole, our model can fully take the advantages
in their correlation. To our best knowledge, we are
the first to tackle graph clustering in a deep probabilistic viewpoint. We perform extensive experiments on both synthetic and real-world networks to
corroborate the effectiveness and efficiency of the
proposed framework