Mixed Latent Position Cluster Models for Networks

TL;DR

This paper introduces the Mixed Latent Position Cluster Model (MLPCM), an advanced network analysis tool that captures asymmetry and non-Euclidean patterns, providing new insights into directed and weighted networks.

Contribution

The paper develops MLPCM, extending the Latent Position Model to handle directed, weighted networks with asymmetric and complex geometric structures, along with an efficient variational Bayes estimation method.

Findings

Accurately models directed and weighted networks.

Effectively disentangles node behavior from perception.

Demonstrates utility on international arms transfer data.

Abstract

Over the last two decades, the Latent Position Model (LPM) has become a prominent tool to obtain model-based visualizations of networks. However, the geometric structure of the LPM is inherently symmetric, in the sense that outgoing and incoming edges are assumed to follow the same statistical distribution. As a consequence, the canonical LPM framework is not ideal for the analysis of directed networks. In addition, edges may be weighted to describe the duration or intensity of a connection. This can lead to disassortative patterns and other motifs that cannot be easily captured by the underlying geometry. To address these limitations, we develop a novel extension of the LPM, called the Mixed Latent Position Cluster Model (MLPCM), which can deal with asymmetry and non-Euclidean patterns, while providing new interpretations of the latent space. We dissect the directed edges of the network by formally disentangling how a node behaves from how it is perceived by others. This leads to a dual representation of a node's profile, identifying its ``overt'' and ``covert'' social positions. In order to efficiently estimate the parameters of our model, we develop a variational Bayes approach to approximate the posterior distribution. Unlike many existing variational frameworks, our algorithm does not require any additional numerical approximations. Model selection is performed by introducing a novel partially integrated complete likelihood criteria, which builds upon the literature on penalized likelihood methods. We demonstrate the accuracy of our proposed methodology using synthetic datasets, and we illustrate its practical utility with an application to a dataset of international arms transfers.