Latent Process Models for Functional Network Data
Authors: Peter W. MacDonald, Elizaveta Levina, Ji Zhu
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We derive a gradient descent estimation algorithm, establish theoretical guarantees for recovery of the low dimensional structure, compare our method to competitors, and apply it to a data set of international political interactions over time, showing our proposed method to adapt well to data, outperform competitors, and provide interpretable and meaningful results. Keywords: Latent space model, Multilayer, Multiplex, Dynamic network, B-spline |
| Researcher Affiliation | Academia | Peter W. Mac Donald EMAIL Department of Statistics & Actuarial Science University of Waterloo Waterloo, ON N2L 3G1, Canada Elizaveta Levina EMAIL Ji Zhu EMAIL Department of Statistics University of Michigan Ann Arbor, MI 48109-1107, USA |
| Pseudocode | Yes | Algorithm 1: Concurrent gradient descent algorithm. Algorithm 2: Local average embedding initialization algorithm. Algorithm 3: Sequential gradient descent algorithm. |
| Open Source Code | Yes | FASE is implemented in an R package fase available on CRAN. |
| Open Datasets | Yes | As an application to real functional network data, we apply FASE to data collected by the Integrated Crisis Early Warning System (ICEWS) (Lautenschlager et al., 2015). ... URL https://doi.org/10.7910/DVN/28117. |
| Dataset Splits | Yes | We then uniformly select a random snapshot index x k [0.25, 0.5] (to avoid boundary effects) and remove it along with M snapshots immediately before and after x k, for M = 0, 1, ..., 10. That is, we treat the 2M +1 network snapshots closest to the selected snapshot in the index space as missing. |
| Hardware Specification | Yes | As an example benchmark, fitting FASE with n = m = 100, d = 2 and q = 10 to data generated as in scenario (i) takes about 4 seconds on a computer with an Apple M2 Pro Chip and 16GB RAM. |
| Software Dependencies | No | FASE is implemented in an R package fase available on CRAN. ... Implementations of both concurrent and sequential FASE estimators are available in the R package fase. |
| Experiment Setup | Yes | In these simulations, we set the convergence criterion for Algorithm 1 to the relative decrease in the objective function dropping below 10 5. ... In the coordinate descent scheme, we initialize d = 1, and perform alternating minimization for q and d by evaluating NGCV on a grid and treating the other as fixed. |