# Principled Multilayer Network Embedding

arXiv: https://arxiv.org/abs/1709.03551 · HTML: https://mlsanity.com/paper/1709.03551/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 24 of 24)

- [HDMI: High-order Deep Multiplex Infomax](https://mlsanity.com/paper/2102.07810/) (2021)
- [Multi-view Contrastive Graph Clustering](https://mlsanity.com/paper/2110.11842/) (2021)
- [Multiplex Heterogeneous Graph Convolutional Network](https://mlsanity.com/paper/2208.06129/) (2022)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Multilayer Graph Contrastive Clustering Network](https://mlsanity.com/paper/2112.14021/) (2021)
- [Variational Graph Generator for Multi-View Graph Clustering](https://mlsanity.com/paper/2210.07011/) (2022)
- [Semi-Supervised Deep Learning for Multiplex Networks](https://mlsanity.com/paper/2110.02038/) (2021)
- [Instant Representation Learning for Recommendation over Large Dynamic Graphs](https://mlsanity.com/paper/2305.18622/) (2023)
- [Scalable Multi-view Clustering with Graph Filtering](https://mlsanity.com/paper/2205.09228/) (2022)
- [Dual Information Enhanced Multi-view Attributed Graph Clustering](https://mlsanity.com/paper/2211.14987/) (2022)
- [Zoo Guide to Network Embedding](https://mlsanity.com/paper/2305.03474/) (2023)
- [Relation-Aware Distribution Representation Network for Person Clustering with Multiple Modalities](https://mlsanity.com/paper/2308.00588/) (2023)
- [Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding](https://mlsanity.com/paper/2312.16834/) (2023)
- [Interpretable Network Representation Learning with Principal Component Analysis](https://mlsanity.com/paper/2106.14238/) (2021)
- [MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction](https://mlsanity.com/paper/2206.13223/) (2022)
- [Audience Expansion for Multi-show Release Based on an Edge-prompted Heterogeneous Graph Network](https://mlsanity.com/paper/2304.05474/) (2023)
- [MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding](https://mlsanity.com/paper/2403.19246/) (2024)
- [HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks](https://mlsanity.com/paper/2410.02087/) (2024)
- [Perturbation Ontology based Graph Attention Networks](https://mlsanity.com/paper/2411.18520/) (2024)
- [MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction](https://mlsanity.com/paper/2504.17749/) (2025)
- [A Transfer Learning Framework for Multilayer Networks via Model Averaging](https://mlsanity.com/paper/2506.12455/) (2025)
- [Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Voting](https://mlsanity.com/paper/2508.21236/) (2025)
- [Causality-Driven Disentangled Representation Learning in Multiplex Graphs](https://mlsanity.com/paper/2603.24105/) (2026)
- [T-GINEE: A Tensor-Based Multilayer Graph Representation Learning](https://mlsanity.com/paper/2605.28300/) (2026)

## Abstract (arXiv)

Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a single-layer network, network embedding methods have been proposed to project the nodes in a network into a continuous vector space with a relatively small number of dimensions, where the space embeds the social representations among nodes. These algorithms have been proved to have a better performance on a variety of regular graph analysis tasks, such as link prediction, or multi-label classification. In this paper, by extending a standard graph mining into multilayer network, we have proposed three methods ("network aggregation," "results aggregation" and "layer co-analysis") to project a multilayer network into a continuous vector space. From the evaluation, we have proved that comparing with regular link prediction methods, "layer co-analysis" achieved the best performance on most of the datasets, while "network aggregation" and "results aggregation" also have better performance than regular link prediction methods.