An Eigenmodel for Dynamic Multilayer Networks
Authors: Joshua Daniel Loyal, Yuguo Chen
JMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the estimation procedure s accuracy and scalability on simulated networks. We apply the model to two real-world problems: discerning regional conflicts in a data set of international relations and quantifying infectious disease spread throughout a school based on the student s daily contact patterns. |
| Researcher Affiliation | Academia | Joshua Daniel Loyal EMAIL Department of Statistics Florida State University Tallahassee, FL 32308, USA Yuguo Chen EMAIL Department of Statistics University of Illinois at Urbana-Champaign Champaign, IL 61820, USA |
| Pseudocode | Yes | Algorithm 1: Coordinate ascent variational inference for the eigenmodel for dynamic multilayer networks. Appendix C contains the details of Algorithms 2 5. |
| Open Source Code | Yes | A repository for the replication code is available on Github (Loyal, 2023). |
| Open Datasets | Yes | The raw data consists of (source actor, target actor, event type, time-stamp) tuples collected by the Integrated Crisis Early Warning System (ICEWS) project (Boschee et al., 2015)... The contact networks were collected by the Socio Patterns collaboration (http://www.sociopatterns.org) and initially analyzed in Stehl e et al. (2011). |
| Dataset Splits | Yes | Specifically, we randomly labeled 20% of the dyads as held-out data and removed them during estimation. |
| Hardware Specification | Yes | The machine used for these benchmarks was a 2021 Mac Book Pro with an Apple M1 Pro processor and a memory of 32 GB. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies with version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | We set ϵ = 0.05 and E [τ 2 δ ] = 10. For σ2 δ, we set cσ2 δ = 2 and dσ2 δ = 2. For the latent position s variational distributions, we set the variances and cross-covariances to the identity matrix Id. We chose uninformative priors for the inverse-Wishart distribution by setting ν = 2 + d and V = Id. For σ2, we set cσ2 = 2 and dσ2 = 2. Lastly, for the reference layer, we set ρ = 1/2 and Σλk = 10Id. In addition, we set the prior variance to σ2 λ = 4. Lastly, we initialized the mean of the P olya-gamma random variables, µωk ijt, to zero. |