Abstract
Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2)
obtaining consensus partition with learnable graph
representations is still under-explored. In this paper, we propose a novel Adversarial Graph AutoEncoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network
is adopted as probabilistic encoder to jointly integrate the information from feature content and
consensus graph, and a simple inner product layer
is used as decoder to reconstruct graph with the
encoded latent variables (i.e., embedding representations). Moreover, we develop an adversarial
regularizer to guide the network training with an
adaptive partition-dependent prior. Experiments on
eight real-world datasets are presented to show the
effectiveness of AGAE over several state-of-the-art
deep embedding and ensemble clustering methods