Understanding Information Diffusion under Interactions
Authors: Yuan Su, Xi Zhang, Philip S. Yu, Wen Hua, Xiaofang Zhou, Binxing Fang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with large-scale Weibo dataset demonstrate that IAD outperforms the state-of-art baselines in terms of F1-score and accuracy, as well as the runtime for learning. |
| Researcher Affiliation | Academia | Beijing University of Posts and Telecommunications, China University of Illinois at Chicago, USA Institute for Data Science, Tsinghua University, China The University of Queensland, Australia Soochow University, China |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | Yes | The Weibo dataset [Zhang et al., 2013] provides a list of Weibo users who have forwarded contagions, as well as the forwarding timestamp. |
| Dataset Splits | No | The paper mentions '90% of the instances as the training set, and the remaining 10% as the testing set' but does not explicitly mention a separate validation split. |
| Hardware Specification | Yes | All experiments are performed on a dual-core Xeon E5-2690 v2 processor. |
| Software Dependencies | No | The paper mentions using 'LIBSVM [Chang and Lin, 2011]' and 'LDA [Blei et al., 2003]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We set the number of latent topics set |t| = 20, 30 and 50 respectively. ... we set K = 1 and 2. ... we set a predicting result to 0 if the predicting infection probability is less than 0.5, otherwise we set the predicting result to 1. ... Stochastic gradient ascent is adopted to fit the model. |