Reconstructing Sparse Multiplex Networks with Application to Covert Networks

TL;DR

This paper introduces an EMA framework that effectively reconstructs complete multiplex network structures from partial data, aiding in covert network analysis and resource allocation.

Contribution

The paper presents a novel EMA framework integrating the configuration model for improved multiplex network reconstruction, especially in covert network contexts.

Findings

EMA outperforms EM and random models in accuracy

Performance gain decreases as layers increase

Inferred networks assist covert network monitoring and resource allocation

Abstract

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.