# MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction

arXiv: https://arxiv.org/abs/2504.17749 · HTML: https://mlsanity.com/paper/2504.17749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [A Survey on Oversmoothing in Graph Neural Networks](https://mlsanity.com/paper/2303.10993/) (2023)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification](https://mlsanity.com/paper/1906.02319/) (2019)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [Principled Multilayer Network Embedding](https://mlsanity.com/paper/1709.03551/) (2017)
- [Multilayer Networks in a Nutshell](https://mlsanity.com/paper/1804.03488/) (2018)
- [Link prediction in multiplex networks via triadic closure](https://mlsanity.com/paper/2011.09126/) (2020)
- [Multimodal urban mobility and multilayer transport networks](https://mlsanity.com/paper/2111.02152/) (2021)
- [Universal Multilayer Network Exploration by Random Walk with Restart](https://mlsanity.com/paper/2107.04565/) (2021)
- [Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks](https://mlsanity.com/paper/2501.17557/) (2025)
- [Multilayer Brain Networks](https://mlsanity.com/paper/1709.02325/) (2017)
- [NODDLE: Node2vec based deep learning model for link prediction](https://mlsanity.com/paper/2305.16421/) (2023)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have been widely used for various learning tasks, ranging from node classification to link prediction. They have demonstrated excellent performance in multiple domains involving graph-structured data. However, an important but less explored learning task is link weight prediction which is more complex than binary link classification. Link weight prediction becomes even more challenging when considering multilayer networks, where nodes can be connected across multiple layers. To address these challenges, we propose a new method called Multiplex Spatial Graph Convolution Network (MSGCN), which spatially embeds information across multiple layers to predict interlayer link weights. The MSGCN method generalizes spatial graph convolution to multiplex networks and captures the geometric structure of nodes across multiple layers. Extensive experiments using data with known interlayer link information show that the MSGCN model has robust, accurate, and generalizable link weight prediction performance across a wide variety of network structures. We also demonstrate a real-world application of the proposed method using the London transportation network. In this setting, MSGCN accurately predicts passenger loads in the multiplex network, where the interlayer link weights represent the number of passengers traveling between stations that are not directly connected.