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1. The Multiplex p2 Model: Mixed-Effects Modeling for Multiplex Social Networks

   https://doi.org/10.1214/25-BA1527  

   Hong, Anni; Niezink, Nynke (January 2025, Bayesian Analysis)

   Social actors are often embedded in multiple social networks, and there is a growing interest in studying social systems from a multiplex network perspective. In this paper, we propose a mixed-effects model for cross-sectional multiplex network data that assumes dyads to be conditionally independent. Building on the uniplex p2 model, we incorporate dependencies between different network layers via cross-layer dyadic effects and through the covariance structure among the actor random effects. These cross-layer effects model the tendencies for ties between two actors and the ties to and from the same actor to be dependent across different relational dimensions. The model can also study the effect of actor and dyad covariates. As simulation-based goodness-of-fit analyses are common practice in applied network studies, we here propose goodness-of-fit measures for multiplex network analyses. We evaluate our choice of priors and the computational faithfulness and inferential properties of the proposed method through simulation. We illustrate the utility of the multiplex p2 model in a replication study of a toxic chemical policy network. An original study that reflects on gossip as perceived by gossip senders and gossip targets, and their differences in perspectives, based on data from 34 Hungarian elementary school classes, highlights the applicability of the proposed method. The proposed methodology is available in the R package multip2. 

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2. Array testing for multiplex assays

   https://doi.org/10.1093/biostatistics/kxy058  

   Hou, Peijie; Tebbs, Joshua M; Wang, Dewei; McMahan, Christopher S; Bilder, Christopher R (October 2018, Biostatistics)

   null (Ed.)

   Summary Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of disease. When the proportion of diseased individuals is small, group testing can greatly reduce the number of tests needed to screen a population. Statistical research in group testing has traditionally focused on applications for a single disease. However, blood service organizations and large-scale disease surveillance programs are increasingly moving towards the use of multiplex assays, which measure multiple disease biomarkers at once. Tebbs and others (2013, Two-stage hierarchical group testing for multiple infections with application to the Infertility Prevention Project. Biometrics69, 1064–1073) and Hou and others (2017, Hierarchical group testing for multiple infections. Biometrics73, 656–665) were the first to examine hierarchical group testing case identification procedures for multiple diseases. In this article, we propose new non-hierarchical procedures which utilize two-dimensional arrays. We derive closed-form expressions for the expected number of tests per individual and classification accuracy probabilities and show that array testing can be more efficient than hierarchical procedures when screening individuals for multiple diseases at once. We illustrate the potential of using array testing in the detection of chlamydia and gonorrhea for a statewide screening program in Iowa. Finally, we describe an R/Shiny application that will help practitioners identify the best multiple-disease case identification algorithm. 

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3. Cosine similarity for multiplex network summarization

   https://doi.org/10.1145/3487351.3488331  

   Polychronopoulou, Athanasia; Zhou, Fang; Obradovic, Zoran (January 2021, ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)
4. Multiplex Conductance and Gossip Based Information Spreading in Multiplex Networks

   https://doi.org/10.1109/TNSE.2018.2838104  

   Huang, Yufan; Dai, Huaiyu (July 2019, IEEE Transactions on Network Science and Engineering)
5. Clustering of Diverse Multiplex Networks

   https://doi.org/10.1109/TNSE.2024.3374102  

   Pensky, Marianna; Wang, Yaxuan (July 2024, IEEE Transactions on Network Science and Engineering)