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.

翻译：无监督多重图学习（UMGL）通过探索多重图间的互补信息与一致信息，已在各类下游任务中展现出显著有效性。然而，现有方法通常忽视了实际应用中的两类问题：样本外问题与噪声问题。针对上述问题，本文提出了一种高效且有效的UMGL方法，旨在同时挖掘互补信息与一致信息。为此，本方法采用多个MLP编码器替代图卷积网络（GCN）进行表征学习，并施加两种约束：保留节点间的局部图结构以解决样本外问题，最大化多节点表征的相关性以解决噪声问题。综合实验表明，本方法在有效性与效率上均优于对比方法，并有效解决了上述两类问题。代码开源地址为 https://github.com/LarryUESTC/CoCoMG。