A Thorough Comparison Between Independent Cascade and Susceptible-Infected-Recovered Models
Authors: Panfeng Liu, Guoliang Qiu, Biaoshuai Tao, Kuan Yang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Specifically, we prove that, given the same network, the same seed sets, and the parameters of the two models being set based on the above-mentioned way, the expected number of infected nodes at the end of the cascade for the IC model is weakly larger than that for the SIR model, and there are instances where this dominance is significant. We design efficient approximation algorithms with theoretical guarantees by adapting the reverse-reachable-set-based algorithms, commonly used for the IC model, to the SIR model. |
| Researcher Affiliation | Academia | Panfeng Liu1, Guoliang Qiu1, Biaoshuai Tao2, Kuan Yang2 1 Department of Computer Science and Engineering, Shanghai Jiao Tong University EMAIL, EMAIL 2 John Hopcroft Center for Computer Science, Shanghai Jiao Tong University EMAIL, EMAIL |
| Pseudocode | No | The paper discusses algorithmic results and algorithm design, stating that 'Details for our algorithms are available in the full version of our paper.' However, the provided text does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions an 'Extended version https://arxiv.org/abs/2408.11470' which is a preprint, not a source code repository. There is no explicit statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | The paper discusses information diffusion in social networks and models like IC and SIR but does not describe or use any specific datasets. Therefore, no access information for public or open datasets is provided. |
| Dataset Splits | No | The paper does not mention using any specific datasets for experimental evaluation, thus no information regarding training/test/validation dataset splits is provided. |
| Hardware Specification | No | The paper focuses on theoretical comparisons of diffusion models and the design of approximation algorithms. It does not describe any experiments that would require specific hardware, and therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper discusses theoretical algorithms and models. It does not provide details on specific software components with version numbers that would be required to replicate experiments. |
| Experiment Setup | No | The paper is theoretical in nature, focusing on model comparisons and algorithm design with theoretical guarantees. It does not describe any experimental setups, hyperparameters, or training configurations. |