User Group Oriented Temporal Dynamics Exploration
Authors: Zhiting Hu, Junjie Yao, Bin Cui
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the performance of Gros To T on a largescale micro-blog dataset consisting of 14M posts generated by 0.52M users, spanning three months period, from Dec 2012 to Feb 2013. Cros Tot shows significant improvement over state-of-the-art temporal modeling methods. Our proposed approach shows advantage not only in temporal dynamics but also group content modeling. |
| Researcher Affiliation | Academia | Zhiting Hu1, Junjie Yao2, Bin Cui1 1Department of Computer Science, Key Lab of High Confidence Software Technologies (MOE), Peking University 2University of California, Santa Barbara zhitinghu@gmail.com, bin.cui@pku.edu.cn, jjyao@cs.ucsb.edu |
| Pseudocode | No | The paper describes the generative process and model inference steps in paragraph and equation form, but does not include a formally labeled 'Pseudocode' or 'Algorithm' block or figure. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the described methodology. |
| Open Datasets | No | We use a large real dataset crawled from Sina Weibo2, one of the most popular micro-blog platforms. We randomly sample users and get their streaming updates. |
| Dataset Splits | No | The paper states 'We randomly select 80% tweets as the training set while the remaining 20% as testing set,' but does not specify a separate validation dataset or split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions like Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | For simplicity we fix the hyperparameters to β = λ = 0.01, δ = 50/C and α = 50/K. For clarity we fix K = 100 and G = 100 in the following study. |