Bandit Sampling for Multiplex Networks
Cenk Baykal, Vamsi K. Potluru, Sameena Shah, Manuela M. Veloso

TL;DR
This paper introduces a scalable online layer sampling algorithm for multiplex graph neural networks, significantly improving efficiency in learning from multiple connection types while maintaining strong predictive performance.
Contribution
It proposes a novel layer sampling method for multiplex networks that reduces computational complexity compared to existing approaches, enabling scalable training on large, multi-layer graphs.
Findings
Efficient layer sampling improves training speed on large multiplex networks.
The method maintains or improves prediction accuracy compared to full-layer aggregation.
Experimental results validate the approach on synthetic and real-world datasets.
Abstract
Graph neural networks have gained prominence due to their excellent performance in many classification and prediction tasks. In particular, they are used for node classification and link prediction which have a wide range of applications in social networks, biomedical data sets, and financial transaction graphs. Most of the existing work focuses primarily on the monoplex setting where we have access to a network with only a single type of connection between entities. However, in the multiplex setting, where there are multiple types of connections, or \emph{layers}, between entities, performance on tasks such as link prediction has been shown to be stronger when information from other connection types is taken into account. We propose an algorithm for scalable learning on multiplex networks with a large number of layers. The efficiency of our method is enabled by an online learning…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Data Stream Mining Techniques · Advanced Graph Neural Networks
