# Adversarial Socialbots Modeling Based on Structural Information Principles

arXiv: https://arxiv.org/abs/2312.08098 · HTML: https://mlsanity.com/paper/2312.08098/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [Structural Entropy Guided Graph Hierarchical Pooling](https://mlsanity.com/paper/2206.13510/) (2022)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization](https://mlsanity.com/paper/2303.09778/) (2023)
- [The Anatomy of a Scientific Rumor](https://mlsanity.com/paper/1301.2952/) (2013)
- [Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling](https://mlsanity.com/paper/1907.11625/) (2019)
- [The DARPA Twitter Bot Challenge](https://mlsanity.com/paper/1601.05140/) (2016)
- [Hierarchical State Abstraction Based on Structural Information Principles](https://mlsanity.com/paper/2304.12000/) (2023)
- [FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://mlsanity.com/paper/2303.07113/) (2023)
- [Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles](https://mlsanity.com/paper/2304.00755/) (2023)
- [Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms](https://mlsanity.com/paper/1902.00043/) (2019)
- [Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection](https://mlsanity.com/paper/1904.05132/) (2019)
- [CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks](https://mlsanity.com/paper/2107.03603/) (2021)
- [Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs](https://mlsanity.com/paper/2309.01899/) (2023)
- [Its All in a Name: Detecting and Labeling Bots by Their Name](https://mlsanity.com/paper/1812.05932/) (2018)
- [An Information-theoretic Perspective of Hierarchical Clustering](https://mlsanity.com/paper/2108.06036/) (2021)
- [Two Evidential Data Based Models for Influence Maximization in Twitter](https://mlsanity.com/paper/1701.05751/) (2017)

## Built on by (most cited first, 10 of 10)

- [SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection](https://mlsanity.com/paper/2405.11225/) (2024)
- [BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers](https://mlsanity.com/paper/2404.15070/) (2024)
- [Effective Exploration Based on the Structural Information Principles](https://mlsanity.com/paper/2410.06621/) (2024)
- [Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning](https://mlsanity.com/paper/2509.21942/) (2025)
- [FairGSE: Fairness-Aware Graph Neural Network without High False Positive Rates](https://mlsanity.com/paper/2511.12132/) (2025)
- [Structural Entropy Guided Probabilistic Coding](https://mlsanity.com/paper/2412.08841/) (2024)
- [Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2505.04339/) (2025)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Semi-Supervised Clustering via Structural Entropy with Different Constraints](https://mlsanity.com/paper/2312.10917/) (2023)
- [Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment](https://mlsanity.com/paper/2601.09051/) (2026)

## Abstract (arXiv)

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.