Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Diffusion Attention Network
Authors: Zhitao Wang, Wenjie Li
IJCAI 2019 | Venue PDF | 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 EMAIL |
| 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. |