# Bandit Sampling for Multiplex Networks

arXiv: https://arxiv.org/abs/2202.03621 · HTML: https://mlsanity.com/paper/2202.03621/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [A Second-order Bound with Excess Losses](https://mlsanity.com/paper/1402.2044/) (2014)
- [Second-order Quantile Methods for Experts and Combinatorial Games](https://mlsanity.com/paper/1502.08009/) (2015)
- [Bandit Samplers for Training Graph Neural Networks](https://mlsanity.com/paper/2006.05806/) (2020)
- [A Biased Graph Neural Network Sampler with Near-Optimal Regret](https://mlsanity.com/paper/2103.01089/) (2021)
- [A Drifting-Games Analysis for Online Learning and Applications to Boosting](https://mlsanity.com/paper/1406.1856/) (2014)

## Built on by (most cited first, 1 of 1)

- [Representing Social Networks as Dynamic Heterogeneous Graphs](https://mlsanity.com/paper/2209.03144/) (2022)

## Abstract (arXiv)

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 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 algorithm that learns how to sample relevant neighboring layers so that only the layers with relevant information are aggregated during training. This sampling differs from prior work, such as MNE, which aggregates information across all layers and consequently leads to computational intractability on large networks. Our approach also improves on the recent layer sampling method of textscDeePlex in that the unsampled layers do not need to be trained, enabling further increases in efficiency.We present experimental results on both synthetic and real-world scenarios that demonstrate the practical effectiveness of our proposed approach.