Hierarchical Diffusion Attention Network
Authors: Zhitao Wang, Wenjie Li
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model against state-of-the-art sequential diffusion prediction models on three real diffusion datasets. The significantly better performance demonstrates the effectiveness of our model. |
| Researcher Affiliation | Academia | Zhitao Wang and Wenjie Li Department of Computing, The Hong Kong Polytechnic University, Hong Kong {csztwang, cswjli}@comp.polyu.edu.hk |
| Pseudocode | No | The paper describes the model architecture and equations but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | Memes [Leskovec et al., 2009]: This dataset contains articles from mainstream news websites or blogs. ... Weibo [Zhang et al., 2013]: This dataset consists of content reposting logs crawled from Sina Weibo, a Chinese microblogging site. ... Twitter [Weng et al., 2013]: This dataset records the diffusion processes of hash-tags in Twitter. |
| Dataset Splits | Yes | We randomly sample 80% of cascades for training and the rest for validating and testing with an even split. |
| Hardware Specification | Yes | All models except CYAN-RNN1 are implemented with Tensorflow and trained on the same GTX1080Ti graphic card with the same batch size. |
| Software Dependencies | No | The paper mentions 'Tensorflow' as the implementation framework and 'Adam optimizer' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | For the proposed models, the dimension size of d is also 64, the learning rate is 0.001, the max observation time Tmax is 120 hours, the number of splitting time interval T is 40, and the non-linear activation functions fx, fu are selected as Exponential Linear Unit (ELU) [Clevert et al., 2016]. We also apply the Dropout [Srivastava et al., 2014] with the keep probability 0.8 and the L2 regularization on parameters to avoid over-fitting. |