# Reconstructing Sparse Multiplex Networks with Application to Covert Networks

arXiv: https://arxiv.org/abs/2208.01739 · HTML: https://mlsanity.com/paper/2208.01739/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 16 of 16)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Inferring Degrees from Incomplete Networks and Nonlinear Dynamics](https://mlsanity.com/paper/2004.10546/) (2020)
- [Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification](https://mlsanity.com/paper/2110.12035/) (2021)
- [Stochastic Block Models with Multiple Continuous Attributes](https://mlsanity.com/paper/1803.02726/) (2018)
- [Random Walks on Multiplex Networks](https://mlsanity.com/paper/1306.0519/) (2013)
- [Growing multiplex networks](https://mlsanity.com/paper/1302.7126/) (2013)
- [Disrupting Resilient Criminal Networks through Data Analysis: The case of Sicilian Mafia](https://mlsanity.com/paper/2003.05303/) (2020)
- [Link prediction in multiplex networks via triadic closure](https://mlsanity.com/paper/2011.09126/) (2020)
- [The Infinite Degree Corrected Stochastic Block Model](https://mlsanity.com/paper/1311.2520/) (2013)
- [Network Structure and Resilience of Mafia Syndicates](https://mlsanity.com/paper/1509.01608/) (2015)
- [Interdicting Restructuring Networks with Applications in Illicit Trafficking](https://mlsanity.com/paper/2011.07093/) (2020)
- [Multilayer Network Analysis: The Identification of Key Actors in a Sicilian Mafia Operation](https://mlsanity.com/paper/2105.09397/) (2021)
- [Congestion induced by the structure of multiplex networks](https://mlsanity.com/paper/1602.07474/) (2016)
- [Link Prediction in Real-World Multiplex Networks via Layer Reconstruction Method](https://mlsanity.com/paper/1906.09422/) (2019)

## Abstract (arXiv)

Network structure provides critical information for understanding the dynamic behavior of networks. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.