Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks
Authors: Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, Zhiyuan Liu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science and Technology, Tsinghua University, Beijing, China 4Mila-Quebec Institute for Learning Algorithms, Canada |
| Pseudocode | No | The paper describes algorithms and processes in prose and mathematical equations but does not include a formal pseudocode block or algorithm box. |
| Open Source Code | Yes | The source code of this paper can be found at https://github.com/albertyang33/ FOREST. |
| Open Datasets | Yes | Twitter [Hodas and Lerman, 2014] dataset records the tweets containing URLs during October 2010. ... Douban [Zhong et al., 2012] is a Chinese social website ... Memetracker [Leskovec et al., 2009] collects a million of news stories and blog posts from online websites and track the most frequent quotes and phrases, i.e. memes, to analyze the migration of memes among people. ... For Twitter and Douban datasets, we use pretrained Deep Walk [Perozzi et al., 2014] embedding with dimension d = 64 as initial user feature vectors f (0) v . |
| Dataset Splits | Yes | We randomly sample 80% of cascades for training, 10% for validation and the rest 10% for test. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using Adam [Kingma and Ba, 2015] optimizer, but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | For hyper-parameter settings, the dimension of hidden state and user feature vector d = 64, controlling window size m = 3, neighbors sampled in structural context extraction Z1 = 25, Z2 = 10 for first-order and second-order aggregation, the dimension of positional embedding dpos = 8 and training data are grouped into mini-batches with batch size 16. |