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..
Link Prediction With Personalized Social Influence
Authors: Zepeng Huo, Xiao Huang, Xia Hu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through comprehensive experiments, we demonstrate that the proposed framework can perform better than the state-of-the-art methods on different real-world networks. |
| Researcher Affiliation | Academia | Zepeng Huo,1 Xiao Huang,1 Xia Hu1,2 1Department of Computer Science and Engineering, Texas A&M University 2Center for Remote Health Technologies and Systems, Texas A&M Engineering Experiment Station |
| Pseudocode | No | The paper describes the proposed framework and algorithms using mathematical equations and textual explanations, but it does not include a formally labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper does not include any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We use two publicly available datasets in our experiment: URL Twitter dataset (Hodas and Lerman 2014) and Higgs Twitter dataset (Leskovec and Krevl 2014). |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., "10%, 20%, 40%, 60%" for training, and remaining instances for test), but it does not explicitly mention a separate "validation" set or its split percentage/size. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. It only discusses the datasets and experimental setup. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers that would be needed to reproduce the experiments (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | We set the learning rate as 0.01 and regularization parameters of representations as 0.025. Numerically, for the coefficients α = {α0, α1, α2} in Eqs. (5) and (7), a negative coefficient was more likely because of over-fitting than the situation that user uj will suppress user ui s activities. Therefore, if any coefficient happens to be negative in likelihood function, it will be rejected. |