Missing data in multiplex networks: a preliminary study

TL;DR

This paper investigates the impact of missing data on multiplex networks, highlighting the significance of data gaps in multi-layer social network analysis and providing initial experimental insights.

Contribution

It is the first study to analyze how missing data affects multiplex network measures, exploring causes and effects with experimental evidence.

Findings

Missing data significantly distorts network measures.

Different types of missingness have varied impacts.

Initial experiments show the importance of addressing missing data.

Abstract

A basic problem in the analysis of social networks is missing data. When a network model does not accurately capture all the actors or relationships in the social system under study, measures computed on the network and ultimately the final outcomes of the analysis can be severely distorted. For this reason, researchers in social network analysis have characterised the impact of different types of missing data on existing network measures. Recently a lot of attention has been devoted to the study of multiple-network systems, e.g., multiplex networks. In these systems missing data has an even more significant impact on the outcomes of the analyses. However, to the best of our knowledge, no study has focused on this problem yet. This work is a first step in the direction of understanding the impact of missing data in multiple networks. We first discuss the main reasons for missingness in these systems, then we explore the relation between various types of missing information and their effect on network properties. We provide initial experimental evidence based on both real and synthetic data.