Power-law Distribution Aware Trust Prediction
Authors: Xiao Wang, Ziwei Zhang, Jing Wang, Peng Cui, Shiqiang Yang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on real-world trust networks demonstrate the superior performance of our proposed method over the state-of-the-arts. |
| Researcher Affiliation | Academia | 1School of Computer Science, Beijing University of Posts and Telecommunications, China 2Department of Computer Science and Technology, Tsinghua University, China 3Graduate School of Information Science and Technology, The University of Tokyo, Japan |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We employed the following three real world trust networks for the evaluations: Advogato [Massa et al., 2009], Ciao [Tang and Liu, 2015], and Epinions [Tang and Liu, 2015]. |
| Dataset Splits | No | The paper describes training and testing sets with percentages ("randomly chose x% of B as the training set Q and the remaining 1 x% of B as the testing set N"), but does not explicitly mention a validation set or its split details. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not list any software dependencies with specific version numbers. |
| Experiment Setup | Yes | For the low rank based methods, we uniformly set the rank k = 100 for all the networks. For the parameters in our model, we also uniformly tuned λ1, λ2, λ3, λ4 from {0.01, 0.1, 1, 10, 100}. To simplify and shrink the parameter space, we let λ1 = λ2 and λ5 = 100, although the simplification may very likely exclude the best performance of our method. |