Link Prediction With Personalized Social Influence
Authors: Zepeng Huo, Xiao Huang, Xia Hu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |