A sharp NMF result with applications in network modeling
Authors: Jiashun Jin
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now consider some real examples. The weblog is a well-known data set [22], where with some light preprocessing, the network has 1, 222 node (each is a blog) and 16, 714 edges (each is a two-way hyperlink). The network has two communities: democratic and republican. For this data set, a rank-2 model is appropriate, so we have (n, K) = (1, 222, 2) (e.g., [30, 12, 18]). |
| Researcher Affiliation | Academia | Jiashun Jin Department of Statistics & Data Science Carnegie Mellon University Pittsburgh, PA 15213 jiashun@stat.cmu.edu |
| Pseudocode | No | The paper describes an approach in Section 4 with numbered steps but does not present it as formal pseudocode or an algorithm block. |
| Open Source Code | No | The self-evaluation section states: "Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] The data sets we use are well-known public data sets. The algorithm details are given in Section 4." |
| Open Datasets | Yes | The weblog is a well-known data set [22], where with some light preprocessing, the network has 1, 222 node (each is a blog) and 16, 714 edges (each is a two-way hyperlink). The network has two communities: democratic and republican. For this data set, a rank-2 model is appropriate, so we have (n, K) = (1, 222, 2) (e.g., [30, 12, 18]). |
| Dataset Splits | No | The paper discusses using datasets for analysis but does not specify train/validation/test splits, percentages, or absolute sample counts for each split. |
| Hardware Specification | No | The self-evaluation section states: "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]" The paper does not mention specific hardware used for experiments. |
| Software Dependencies | No | The self-evaluation section states: "Did you specify all the training details (e.g., data splits, hyper-parameters, how they were chosen)? [Yes]" However, the main text does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The self-evaluation section states: "Did you specify all the training details (e.g., data splits, hyper-parameters, how they were chosen)? [Yes]" Section 4 outlines the "approach" for estimating parameters and checking conditions, which serves as the experimental setup. |