Electronic Resource

A zero-inflated Poisson latent position cluster model

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The Latent Position Model (LPM) is a popular approach for the statistical analysis of network data. A central aspect of this model is that it assigns nodes to random positions in a latent space, such that the probability of an interaction between each pair of individuals or nodes is determined by their distance in this latent space. A key feature of this model is that it allows one to visualize nuanced structures via the latent space epresentation. The LPM can be further extended to the Latent Position Cluster Model (LPCM), to accommodate the clustering of nodes by assuming that the latent positions are distributed following a finite mixture distribution. In this paper, we extend the LPCM to accommodate missing network data and apply this to non-negative discrete weighted social networks. By treating missing data as “unusual” zero interactions, we propose a combination of the LPCM with the zero-inflated Poisson distribution. Statistical inference is based on a novel partially collapsed Markov chain Monte Carlo algorithm, where a Mixture-of-Finite-Mixtures (MFM) model is adopted to automatically determine the number of
clusters and optimal group partitioning. Our algorithm features a truncated absorb-eject move, which is a novel adaptation of an idea commonly used in collapsed samplers, within the context of MFMs. Another aspect of our work is that we illustrate our results on 3-dimensional latent spaces, maintaining clear visualizations while achieving more flexibility than 2-dimensional models. The performance of this approach is illustrated via three carefully designed simulation studies, as well as four different publicly available real networks, where some interesting new perspectives are uncovered.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

Ireand : Cambridge University Press., 2026

Deskripsi Fisik

37 hlm.

Bahasa

English

ISBN/ISSN

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Klasifikasi

NONE

Tipe Isi

text

Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

Social Networks
Weighted Networks

Info Detail Spesifik

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Pernyataan Tanggungjawab

Chaoyi Lu, Riccardo Rastelli, and Nial Friel

Versi lain/terkait

Lampiran Berkas

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