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..
Adversarial Socialbots Modeling Based on Structural Information Principles
Authors: Xianghua Zeng, Hao Peng, Angsheng Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive and comparative experiments on both homogeneous and heterogeneous social networks demonstrate that, compared with state-of-the-art baselines, the proposed SIASM framework yields substantial performance improvements in terms of network influence (up to 16.32%) and sustainable stealthiness (up to 16.29%) when evaluated against a robust detector with 90% accuracy. |
| Researcher Affiliation | Academia | 1 State Key Laboratory of Software Development Environment, Beihang University, Beijing, China 2 Zhongguancun Laboratory, Beijing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: The Optimization Algorithm |
| Open Source Code | Yes | Furthermore, all source codes and experimental results are available at an anonymous link1. 1https://github.com/SELGroup/SIASM |
| Open Datasets | Yes | For heterogeneous network analysis, we use the latest Higgs Twitter Dataset (De Domenico et al. 2013), which includes directed multi-relational interactions. Like other works (Le, Tran-Thanh, and Lee 2022), we select 10% of the real-life networks to construct synthetic stochastic networks as the training set and take the remaining 90% of the collected networks as the testing set. |
| Dataset Splits | No | The paper states 'we select 10% of the real-life networks to construct synthetic stochastic networks as the training set and take the remaining 90% of the collected networks as the testing set.' It does not explicitly mention a separate validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA x.x). |
| Experiment Setup | Yes | During the training process on synthetic graphs, we use a default propagation probability (p) of 0.8 and a maximal episode length (Tmax) of 120. In this work, we set the ratio of filtered user vertices and the height of pruned subtrees as 5% and 1, respectively. |