# Deep Partial Multiplex Network Embedding

arXiv: https://arxiv.org/abs/2203.02656 · HTML: https://mlsanity.com/paper/2203.02656/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 75 references that are in the map.
- First posted on arXiv 5 Mar 2022.

## Builds on (most cited first, 30 of 32)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [Deep Cross-Modal Hashing](https://mlsanity.com/paper/1602.02255/) (2016)
- [MAVE: A Product Dataset for Multi-source Attribute Value Extraction](https://mlsanity.com/paper/2112.08663/) (2021)
- [Heterogeneous Hypergraph Embedding for Graph Classification](https://mlsanity.com/paper/2010.10728/) (2020)
- [DANE: Domain Adaptive Network Embedding](https://mlsanity.com/paper/1906.00684/) (2019)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [An Attention-based Collaboration Framework for Multi-View Network Representation Learning](https://mlsanity.com/paper/1709.06636/) (2017)
- [Computationally Tractable Riemannian Manifolds for Graph Embeddings](https://mlsanity.com/paper/2002.08665/) (2020)
- [Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method](https://mlsanity.com/paper/2010.11797/) (2020)
- [Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark](https://mlsanity.com/paper/2004.00216/) (2020)
- [Graph Convolution Machine for Context-aware Recommender System](https://mlsanity.com/paper/2001.11402/) (2020)
- [Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks](https://mlsanity.com/paper/2102.06371/) (2021)
- [Adversarial Directed Graph Embedding](https://mlsanity.com/paper/2008.03667/) (2020)
- [mvn2vec: Preservation and Collaboration in Multi-View Network Embedding](https://mlsanity.com/paper/1801.06597/) (2018)
- [ActiveHNE: Active Heterogeneous Network Embedding](https://mlsanity.com/paper/1905.05659/) (2019)
- [MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach](https://mlsanity.com/paper/2008.10085/) (2020)
- [MEGAN: A Generative Adversarial Network for Multi-View Network Embedding](https://mlsanity.com/paper/1909.01084/) (2019)
- [SINE: Scalable Incomplete Network Embedding](https://mlsanity.com/paper/1810.06768/) (2018)
- [JNET: Learning User Representations via Joint Network Embedding and Topic Embedding](https://mlsanity.com/paper/1912.00465/) (2019)
- [CatGCN: Graph Convolutional Networks with Categorical Node Features](https://mlsanity.com/paper/2009.05303/) (2020)

## Built on by (most cited first, 5 of 5)

- [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)
- [Incomplete Graph Learning: A Comprehensive Survey](https://mlsanity.com/paper/2502.12412/) (2025)
- [The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Framework](https://mlsanity.com/paper/2307.05201/) (2023)
- [Simplicity within biological complexity](https://mlsanity.com/paper/2405.09595/) (2024)

## Abstract (arXiv)

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing interest in network embedding on multiplex data. However, most existing multiplex approaches assume that the data is complete in all views. But in real applications, it is often the case that each view suffers from the missing of some data and therefore results in partial multiplex data. In this paper, we present a novel Deep Partial Multiplex Network Embedding approach to deal with incomplete data. In particular, the network embeddings are learned by simultaneously minimizing the deep reconstruction loss with the autoencoder neural network, enforcing the data consistency across views via common latent subspace learning, and preserving the data topological structure within the same network through graph Laplacian. We further prove the orthogonal invariant property of the learned embeddings and connect our approach with the binary embedding techniques. Experiments on four multiplex benchmarks demonstrate the superior performance of the proposed approach over several state-of-the-art methods on node classification, link prediction and clustering tasks.