Cascade Dynamics Modeling with Attention-based Recurrent Neural Network
Authors: Yongqing Wang, Huawei Shen, Shenghua Liu, Jinhua Gao, Xueqi Cheng
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real world datasets demonstrate the proposed models outperform state-of-the-art models at both cascade prediction and inferring diffusion tree. In experiments, we compare our CYAN-RNN to the state-of-the-art modeling methods of cascade prediction on both synthetic and real data. |
| Researcher Affiliation | Academia | 1CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | No | The paper describes the model architecture and optimization process using mathematical formulas and prose, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The real data is from Sina Weibo, a Chinese microblog website. The data is from June 1st, 2016 to June 30th, 2016. ... The paper does not provide concrete access information for this dataset or the synthetic datasets. |
| Dataset Splits | Yes | For synthetic data: "we randomly pick up 80% of cascades for training and the rest for validation and test by an even split." For real data: "We use 536,240 sequences for training, 29,758 for validation and 30,005 for testing." |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adam for optimization and GRU, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The hyper-parameters of CYAN-RNN and CYANRNN(cov) are set as follows: learning rate is 0.0001; hidden layer size of encoder is 20; hidden layer size of decoder is 10; length of dependence is 200; coverage size is 10; and batch size is 128. We apply stochastic gradient descent (SGD) with minibatch and the parameters are updated by Adam [Kingma and Adam, 2015]. |