# Semi-Supervised Deep Learning for Multiplex Networks

arXiv: https://arxiv.org/abs/2110.02038 · HTML: https://mlsanity.com/paper/2110.02038/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://mlsanity.com/paper/1908.01000/) (2019)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations](https://mlsanity.com/paper/1911.07979/) (2019)
- [Unsupervised Attributed Multiplex Network Embedding](https://mlsanity.com/paper/1911.06750/) (2019)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Principled Multilayer Network Embedding](https://mlsanity.com/paper/1709.03551/) (2017)
- [Structure-Feature based Graph Self-adaptive Pooling](https://mlsanity.com/paper/2002.00848/) (2020)
- [Column Networks for Collective Classification](https://mlsanity.com/paper/1609.04508/) (2016)
- [MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks](https://mlsanity.com/paper/1811.08800/) (2018)
- [Meta Path-Based Collective Classification in Heterogeneous Information Networks](https://mlsanity.com/paper/1305.4433/) (2013)

## Built on by (most cited first, 6 of 6)

- [Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs](https://mlsanity.com/paper/2304.06336/) (2023)
- [Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding](https://mlsanity.com/paper/2312.16834/) (2023)
- [A Geometric Perspective for High-Dimensional Multiplex Graphs](https://mlsanity.com/paper/2501.17374/) (2025)
- [3D-IDS: Doubly Disentangled Dynamic Intrusion Detection](https://mlsanity.com/paper/2307.11079/) (2023)
- [Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification](https://mlsanity.com/paper/2605.12699/) (2026)
- [Synchronous Multi-view Neural Diffusion](https://mlsanity.com/paper/2609.39019/) (2026)

## Abstract (arXiv)

Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer. Such graphs are used to investigate many complex biological, social, and technological systems. In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks. Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures jointly. Specifically, it leverages a novel cluster-aware, node-contextualized global graph summary generation strategy for effective joint-modeling of node and cluster representations across the layers of a multiplex network. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art methods in a range of tasks: classification, clustering, visualization, and similarity search on seven real-world multiplex networks for various experiment settings.