Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model
Authors: Yik Lun Kei, Jialiang Li, Hangjian Li, Yanzhen Chen, OSCAR HERNAN MADRID PADILLA
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation studies show good performance of the latent space model in supporting change point detection and real data experiments yield change points that align with significant events. |
| Researcher Affiliation | Collaboration | Yik Lun Kei EMAIL Department of Statistics University of California, Santa Cruz; Jialiang Li EMAIL Department of Computer Science New Jersey Institute of Technology; Hangjian Li EMAIL Walmart Global Tech; Yanzhen Chen EMAIL Department of Information Systems, Business Statistics and Operations Management Hong Kong University of Science and Technology; Oscar Hernan Madrid Padilla EMAIL Department of Statistics and Data Science University of California, Los Angeles |
| Pseudocode | Yes | Algorithm 1 Latent Space Group Fused Lasso |
| Open Source Code | Yes | The codes are available at https://github.com/allenkei/CPD_generative. |
| Open Datasets | Yes | The Massachusetts Institute of Technology (MIT) cellphone data (Eagle & Pentland, 2006) depicts human interactions via phone call activities among n = 96 participants spanning T = 232 days.; The Enron email data, analyzed by Priebe et al. (2005), Park et al. (2012), and Peel & Clauset (2015), portrays the communication patterns among employees before the collapse of a giant energy company. |
| Dataset Splits | Yes | In particular, we split the original time series of graphs into training and testing sets: the training set consists of graphs at odd indexed time points and the testing set consists of graphs at even indexed time points. For the MIT cellphone data with T = 232, we remove the graphs at time t equals to multiples of t = {15, 20, 25, 30} respectively. For the Enron Email data with T = 100, we remove the graphs at time t equals to multiples of t = {3, 6, 9, 12} respectively. |
| Hardware Specification | Yes | We run our experiment with Tesla T4 GPU. |
| Software Dependencies | No | The paper mentions the 'Adam optimizer' and 'nett package (Amini et al., 2013) in R' but does not provide specific version numbers for any software components or libraries. |
| Experiment Setup | Yes | For Langevin Dynamic sampling, we set δ = 0.5, and we draw s = 200 samples for each time point t. To detect change points using the data-driven threshold in (15), we let the tuning parameter λ = {10, 20, 50, 100}. To detect change points using the localizing method with Gamma distribution in (13), we let the tuning parameter λ = {5, 10, 20, 50}. For each λ, we run A = 50 iterations of ADMM. Within each ADMM iteration, we run B = 20 iterations of gradient descent with Adam optimizer for the graph decoder and D = 20 iterations of block coordinate descent for Group Lasso. We set ϵtol = 10−5 and a = 5. Throughout, we initialize the penalty parameter κ = 10. |