Unsupervised Multiplex Graph Learning with Complementary and Consistent
  Information
Liang Peng Xin Wang Xiaofeng Zhu
Unsupervised multiplex graph learning (UMGL) has been shown to achieve
significant effectiveness for different downstream tasks by exploring both
complementary information and consistent information among multiple graphs.
However, previous methods usually overlook the issues in practical
applications, i.e., the out-of-sample issue and the noise issue. To address the
above issues, in this paper, we propose an effective and efficient UMGL method
to explore both complementary and consistent information. To do this, our
method employs multiple MLP encoders rather than graph convolutional network
(GCN) to conduct representation learning with two constraints, i.e., preserving
the local graph structure among nodes to handle the out-of-sample issue, and
maximizing the correlation of multiple node representations to handle the noise
issue. Comprehensive experiments demonstrate that our proposed method achieves
superior effectiveness and efficiency over the comparison methods and
effectively tackles those two issues. Code is available at
https://github.com/LarryUESTC/CoCoMG.