# Embedding Learning on Multiplex Networks for Link Prediction

arXiv: https://arxiv.org/abs/2602.01922 · HTML: https://mlsanity.com/paper/2602.01922/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 75 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 30 of 33)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://mlsanity.com/paper/1710.02971/) (2017)
- [Predicting multicellular function through multi-layer tissue networks](https://mlsanity.com/paper/1707.04638/) (2017)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [Unsupervised Attributed Multiplex Network Embedding](https://mlsanity.com/paper/1911.06750/) (2019)
- [Is Cosine-Similarity of Embeddings Really About Similarity?](https://mlsanity.com/paper/2403.05440/) (2024)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [VERSE: Versatile Graph Embeddings from Similarity Measures](https://mlsanity.com/paper/1803.04742/) (2018)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words](https://mlsanity.com/paper/2205.05092/) (2022)
- [Layer aggregation and reducibility of multilayer interconnected networks](https://mlsanity.com/paper/1405.0425/) (2014)
- [An Attention-based Collaboration Framework for Multi-View Network Representation Learning](https://mlsanity.com/paper/1709.06636/) (2017)
- [The Anatomy of a Scientific Rumor](https://mlsanity.com/paper/1301.2952/) (2013)
- [Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty](https://mlsanity.com/paper/1901.11213/) (2019)
- [Principled approach to the selection of the embedding dimension of networks](https://mlsanity.com/paper/2004.09928/) (2020)
- [A Broader Picture of Random-walk Based Graph Embedding](https://mlsanity.com/paper/2110.12344/) (2021)
- [MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach](https://mlsanity.com/paper/2008.10085/) (2020)
- [MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks](https://mlsanity.com/paper/1811.08800/) (2018)
- [MEGAN: A Generative Adversarial Network for Multi-View Network Embedding](https://mlsanity.com/paper/1909.01084/) (2019)
- [A Practical Tutorial on Graph Neural Networks](https://mlsanity.com/paper/2010.05234/) (2020)
- [Characterizing interactions in online social networks during exceptional events](https://mlsanity.com/paper/1506.09115/) (2015)
- [Multi-View Collaborative Network Embedding](https://mlsanity.com/paper/2005.08189/) (2020)
- [Universal Multilayer Network Exploration by Random Walk with Restart](https://mlsanity.com/paper/2107.04565/) (2021)

## Abstract (arXiv)

Over the past years, embedding learning on networks has shown tremendous results in link prediction tasks for complex systems, with a wide range of real-life applications. Learning a representation for each node in a knowledge graph allows us to capture topological and semantic information, which can be processed in downstream analyses later. In the link prediction task, high-dimensional network information is encoded into low-dimensional vectors, which are then fed to a predictor to infer new connections between nodes in the network. As the network complexity (that is, the numbers of connections and types of interactions) grows, embedding learning turns out increasingly challenging. This review covers published models on embedding learning on multiplex networks for link prediction. First, we propose refined taxonomies to classify and compare models, depending on the type of embeddings and embedding techniques. Second, we review and address the problem of reproducible and fair evaluation of embedding learning on multiplex networks for the link prediction task. Finally, we tackle evaluation on directed multiplex networks by proposing a novel and fair testing procedure. This review constitutes a crucial step towards the development of more performant and tractable embedding learning approaches for multiplex networks and their fair evaluation for the link prediction task. We also suggest guidelines on the evaluation of models, and provide an informed perspective on the challenges and tools currently available to address downstream analyses applied to multiplex networks.