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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |