# MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding

arXiv: https://arxiv.org/abs/2403.19246 · HTML: https://mlsanity.com/paper/2403.19246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (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)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [A Gentle Introduction to Deep Learning for Graphs](https://mlsanity.com/paper/1912.12693/) (2019)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [Graph Representation Learning: A Survey](https://mlsanity.com/paper/1909.00958/) (2019)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data](https://mlsanity.com/paper/2003.12590/) (2020)
- [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)
- [Principled Multilayer Network Embedding](https://mlsanity.com/paper/1709.03551/) (2017)
- [mvn2vec: Preservation and Collaboration in Multi-View Network Embedding](https://mlsanity.com/paper/1801.06597/) (2018)
- [Tensor Graph Convolutional Networks for Multi-relational and Robust Learning](https://mlsanity.com/paper/2003.07729/) (2020)
- [Structural measures for multiplex networks](https://mlsanity.com/paper/1308.3182/) (2013)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks](https://mlsanity.com/paper/2210.08811/) (2022)
- [Interlayer link prediction in multiplex social networks: an iterative degree penalty algorithm](https://mlsanity.com/paper/2002.02140/) (2020)
- [Matching Entities Across Different Knowledge Graphs with Graph Embeddings](https://mlsanity.com/paper/1903.06607/) (2019)

## Abstract (arXiv)

Graph representation learning has rapidly emerged as a pivotal field of study. Despite its growing popularity, the majority of research has been confined to embedding single-layer graphs, which fall short in representing complex systems with multifaceted relationships. To bridge this gap, we introduce MPXGAT, an innovative attention-based deep learning model tailored to multiplex graph embedding. Leveraging the robustness of Graph Attention Networks (GATs), MPXGAT captures the structure of multiplex networks by harnessing both intra-layer and inter-layer connections. This exploitation facilitates accurate link prediction within and across the network's multiple layers. Our comprehensive experimental evaluation, conducted on various benchmark datasets, confirms that MPXGAT consistently outperforms state-of-the-art competing algorithms.