Bayesian Spiked Laplacian Graphs
Authors: Leo L Duan, George Michailidis, Mingzhou Ding
JMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the performance of the methodology on synthetic data sets, as well as a neuroscience study related to brain activity in working memory. Keywords: Isoperimetric Constant, Mixed-Effect Eigendecomposition, Normalized Graph Cut, Stiefel Manifold. Section 5 evaluates the model performance based on synthetic data, while Section 6 illustrates the modeling approach in a data application aiming to characterize the heterogeneity in brain scans in a human working memory study. |
| Researcher Affiliation | Academia | Leo L Duan EMAIL Department of Statistics, University of Florida George Michailidis EMAIL Department of Statistics, University of Florida Mingzhou Ding EMAIL Department of Biomedical Engineering, University of Florida |
| Pseudocode | Yes | Algorithm 1 Sign-based κ-partitioning. Initialize: V[1]1 = {1, . . . , n}, re-order { qk}T k=1 according to non-descending order of λk, denoted by { q(k)}T k=1. for k = 1 to (κ 1) do 1. Compute the sign-based partitioning loss when dividing the [k]lth existing partition, for l = 1, . . . , k: loss[k]l = X q(k)(i)q(k)(j) 1 q(k)(i)q(k)(j) < 0 . |
| Open Source Code | Yes | The software implementation can be found on https://github.com/leoduan/Bayes Spiked Laplacian. |
| Open Datasets | No | Section 6. Data Application: Characterizing Heterogeneity in a Human Working Memory Study. We employ the proposed spiked graph Laplacian model on data obtained from a neuroscience study on working memory, focusing on human brain functional connectivity (Hu et al., 2019). |
| Dataset Splits | No | The paper describes the generation of synthetic data (e.g., "We generate a weighted graph comprising of 60 vertices and three communities of size 10, 20 and 30 vertices") but does not specify how these, or the real-world neuroscience data, were split into training, validation, or test sets for experimental evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models, memory, or computational resources. |
| Software Dependencies | No | The paper mentions a software implementation on GitHub (https://github.com/leoduan/Bayes Spiked Laplacian) but does not list specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | To simplify computations, we approximate the Dirichlet process mixture model with a truncated version, setting the number of mixture components to g and using Dir(α0/g, . . . , α0/g) (in this paper, we use g = 30). The results obtained are based on an MCMC run of 30, 000 steps, with the first 10, 000 used as the burn-in period. |