Google & J.P. Morgan Propose Advanced Bandit Sampling for Multiplex Networks

Graph neural networks (GNNs) have gained popularity in the AI research community due to their impressive performance in high-impact applications such as drug discovery and social network analyses. Most existing studies on GNNs however have focused on "monoplex" settings (networks with only a single type of connection between entities) and not on multiplex settings (multiple types of connections between entities), which reflect many real-world scenarios. In the new paper Bandit Sampling for Multiplex Networks, a team from Google Research and J.P. Morgan AI Research explores the problem of computationally efficient link prediction in the multiplex setting, introducing an algorithm for scalable learning on multiplex networks with a large number of layers. In evaluations, the proposed method is shown to improve efficiency over prior work such as Multiplex Network Embedding (MNE, Zhang et al., 2018) and the DEEPLEX layer-sampling approach (Potluru et al., 2020). The multiplex network problem can be considered as a graph with many layers, where each layer has nodes neighbouring other layers.