Role-based Multiplex Network Embedding
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26265-26280, 2022.
In recent years, multiplex network embedding has received great attention from researchers. However, existing multiplex network embedding methods neglect structural role information, which can be used to determine the structural similarity between nodes. To overcome this shortcoming, this work proposes a simple, effective, role-based embedding method for multiplex networks, called RMNE. The RMNE uses the structural role information of nodes to preserve the structural similarity between nodes in the entire multiplex network. Specifically, a role-modified random walk is designed to generate node sequences of each node, which can capture both the within-layer neighbors, structural role members, and cross-layer structural role members of a node. Additionally, the variant of RMNE extends the existing collaborative embedding method by unifying the structural role information into our method to obtain the role-based node representations. Finally, the proposed methods were evaluated on the network reconstruction, node classification, link prediction, and multi-class edge classification tasks. The experimental results on eight public, real-world multiplex networks demonstrate that the proposed methods outperform state-of-the-art baseline methods.