Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Iterative Connecting Probability Estimation for Networks
Authors: Yichen Qin, Linhan Yu, Yang Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish desirable theoretical properties for our method, and further justify its superior performance by comparing with existing methods in simulation and real data analysis. 5 Experiments To evaluate the effectiveness of our proposed method, we compare its performance with several popular estimation methods using simulated networks with different features... |
| Researcher Affiliation | Academia | Yichen Qin University of Cincinnati EMAIL Linhan Yu Renmin University of China EMAIL Yang Li Renmin University of China EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative connecting probability estimation method and Algorithm 2 Tuning parameters selection of ICE via edge cross-validation |
| Open Source Code | Yes | We include the code and data in the supplemental material and will publish them online later. |
| Open Datasets | Yes | We analyze a human brain projectome dataset from an experiment of Beijing Normal University in China (Yan et al., 2009)2. The dataset is available on https://Neuro Data.io/, a platform that enables large-scale neurodata storing, analyzing, and modeling. |
| Dataset Splits | Yes | The tuning parameters can be selected by network cross-validation. Randomly sample a subset of edges from E with probability p to obtain the training set of the edges Etrain. Let Eval = E Etrain denote the validation set. |
| Hardware Specification | No | [No] Since our method is computationally feasible for networks with moderate size, we omit this part for brevity. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | As to the size of the similar vertex set s, Zhang et al. (2017) set s = C(n log n)1/2 for each vertex i, where C is recommended set as 1. The performance of the combination (Cit = 0.2, Cest = 1) is the most competitive and even comparable to that of Oracle. Input: observed adjacency matrix A; initial connecting probability estimate b P(0); neighborhood size s; threshold δ0 > 0. |