基于结构信息原理的对抗性社交机器人建模
Adversarial Socialbots Modeling Based on Structural Information Principles
AI总结：
针对对抗性社交机器人建模不完善阻碍主动检测发展的问题，提出基于结构信息原理的SIASM框架，通过异构图、结构熵优化等实现建模，在影响力和隐蔽性上显著优于现有基线。
摘要：
社交机器人模仿人类行为传播虚假信息，凸显了有效检测的重要性，这也引发了社交机器人与检测器之间的持续博弈。尽管被动式检测器发展迅速，但对抗性社交机器人建模的探索仍不完善，严重阻碍了主动式检测器的发展。为解决这一问题，我们提出一种基于数学结构信息原理的对抗性社交机器人建模框架，即SIASM，以实现更精准有效的对抗行为建模。首先，构建异构图整合原始社交网络中的各类用户与丰富活动，并将其动态不确定性度量为结构熵。通过最小化高维结构熵，生成社交网络的层次社区结构，称为最优编码树。其次，设计一种利用分配的结构熵量化影响力的新方法，通过过滤无影响力用户降低SIASM的计算成本。此外，定义了社交机器人与其他用户之间的新条件结构熵，用于指导关注者选择以实现网络影响力最大化。在同构和异构社交网络上开展的大量对比实验表明，与现有最优基线相比，在面对准确率达90%的鲁棒检测器时，所提SIASM框架在网络影响力（最高提升16.32%）和持续隐蔽性（最高提升16.29%）方面均取得了显著性能提升。
英文摘要：
The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socialbots and detectors. Despite the rapid advancement of reactive detectors, the exploration of adversarial socialbot modeling remains incomplete, significantly hindering the development of proactive detectors. To address this issue, we propose a mathematical Structural Information principles-based Adversarial Socialbots Modeling framework, namely SIASM, to enable more accurate and effective modeling of adversarial behaviors. First, a heterogeneous graph is presented to integrate various users and rich activities in the original social network and measure its dynamic uncertainty as structural entropy. By minimizing the high-dimensional structural entropy, a hierarchical community structure of the social network is generated and referred to as the optimal encoding tree. Secondly, a novel method is designed to quantify influence by utilizing the assigned structural entropy, which helps reduce the computational cost of SIASM by filtering out uninfluential users. Besides, a new conditional structural entropy is defined between the socialbot and other users to guide the follower selection for network influence maximization. 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.
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