Diffusion Network Inference from Partial Observations
Authors: Ting Gan, Keqi Han, Hao Huang, Shi Ying, Yunjun Gao, Zongpeng Li7493-7500
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on both synthetic and real-world networks show that our approach can properly handle missing data and accurately uncover diffusion network structures. |
| Researcher Affiliation | Academia | 1School of Computer Science, Wuhan University, China 2College of Computer Science and Technology, Zhejiang University, China |
| Pseudocode | No | The paper describes the steps of the POIND algorithm verbally but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We adopt LFR benchmark graphs (Lancichinetti, Fortunato, and Radicchi 2008) as the synthetic networks. In addition, we adopt two realworld networks: Net Sci (Newman 2006), a co-authorship network containing 379 scientists and 1602 co-authorships, and DUNF (Wang et al. 2014), a microblogging network containing 750 users and 2974 following relationships. |
| Dataset Splits | No | The paper describes simulating diffusion processes and randomly removing a percentage of infection status observations as missing data for evaluation. However, it does not specify explicit train/validation/test splits commonly used in machine learning for model development and evaluation. It assesses the inference algorithm's performance on the inferred network structure. |
| Hardware Specification | Yes | All algorithms in the experiments are implemented in Python, running on a desktop PC with Intel Core i3-6100 CPU at 3.70GHz and 8GB RAM. |
| Software Dependencies | No | The paper mentions implementation in "Python" and use of "fsolve and root functions in the Sci Py package for Python," but does not specify version numbers for Python or the SciPy package. |
| Experiment Setup | Yes | For POIND, the maximum number T of iterations is set to 5, the number m of sampling round is set to 6, and the stop condition for the iterative updates of infection transmission probabilities Θ is Θ[t+1] Θ[t] 0.05. |