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Unsupervised multiplex graph learning (UMGL) has been shown to achievesignificant effectiveness for different downstream tasks by exploring bothcomplementary information and consistent information among multiple graphs.However, previous methods usually overlook the issues in practicalapplications, i.e., the out-of-sample issue and the noise issue. To address theabove issues, in this paper, we propose an effective and efficient UMGL methodto explore both complementary and consistent information. To do this, ourmethod employs multiple MLP encoders rather than graph convolutional network(GCN) to conduct representation learning with two constraints, i.e., preservingthe local graph structure among nodes to handle the out-of-sample issue, andmaximizing the correlation of multiple node representations to handle the noiseissue. Comprehensive experiments demonstrate that our proposed method achievessuperior effectiveness and efficiency over the comparison methods andeffectively tackles those two issues. Code is available athttps://github.com/LarryUESTC/CoCoMG.

Unsupervised Multiplex Graph Learning with Complementary and Consistent Information