Multiplex Network Representation Learning Based on Graph Neural Network
DOI:
https://doi.org/10.53469/jtpes.2024.04(05).03Keywords:
Multiplex network, Network Representation Learning, Graph Neural NetworksAbstract
Network representation learning is receiving increasing attention from scholars. Among them, methods based on graph neural networks have become particularly popular. However, most existing methods currently only focus on networks with a single type of relations. In the real world, networks contain a wealth of diverse information, with multiple types of relationships between nodes. In this paper, we propose a graph neural network-based multiplex network representation learning model (GNMRL). We model nodes within each layer of the multiplex network by aggregating neighbor information. Additionally, since nodes are influenced by different network layers, we integrate node interaction information across network layers. We conducted systematic experiments on four datasets, and the results show that GNMRL outperforms other comparison methods in both link prediction and node classification tasks.
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Copyright (c) 2024 Lianwei Li, Peiyuan Yang, Han Lei, Baoming Wang, Zhou Chen
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
