Network Diffusions via Neural Mean-Field Dynamics
Authors: Shushan He, Hongyuan Zha, Xiaojing Ye
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical study shows that our approach is versatile and robust to variations of the underlying diffusion network models, and significantly outperforms existing approaches in accuracy and efficiency on both synthetic and real-world data. |
| Researcher Affiliation | Academia | Shushan He Mathematics & Statistics Georgia State University Atlanta, Georgia, USA she4@gsu.edu Hongyuan Zha School of Data Science Shenzhen Research Institute of Big Data, CUHK, Shenzhen, China zhahy@cuhk.edu.cn Xiaojing Ye Mathematics & Statistics Georgia State University Atlanta, Georgia, USA xye@gsu.edu |
| Pseudocode | Yes | Algorithm 1 Neural mean-field (NMF) algorithm for network inference and influence estimation |
| Open Source Code | Yes | Our numerical implementation of NMF is available at https://github.com/Shushan He/neural-mf. |
| Open Datasets | Yes | We generate training data consists of K=10,000 cascades, which is formed by 10 sample cascades for each of 1,000 source sets (a source set is generated by randomly selecting 1 to 10 nodes from the network). All networks and cascades are generated by SNAP [29]. Our numerical implementation of NMF is available at https://github.com/Shushan He/neural-mf. ... We also tested NMF on a real dataset [54] from Sina Weibo social platform... |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology for train/validation/test sets) was provided for reproduction purposes. While training and testing data are mentioned, a dedicated validation set or precise split ratios are not clearly specified. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as library or solver names with their corresponding versions (e.g., Python 3.8, TensorFlow 2.x). |
| Experiment Setup | Yes | For Influ Learner, we set 128 as the feature number for optimal accuracy as suggested in [12]. For LSTM, we use one LSTM block and a dense layer for each t. ... For each distribution, we draw αji from Unif[0.1,1] to simulate the varying interactions between nodes. |