# BSG4Bot: Efficient Bot Detection based on Biased Heterogeneous Subgraphs

arXiv: https://arxiv.org/abs/2410.05356 · HTML: https://mlsanity.com/paper/2410.05356/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 64 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 30 of 34)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Geom-GCN: Geometric Graph Convolutional Networks](https://mlsanity.com/paper/2002.05287/) (2020)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Adaptive Universal Generalized PageRank Graph Neural Network](https://mlsanity.com/paper/2006.07988/) (2020)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [Is Homophily a Necessity for Graph Neural Networks?](https://mlsanity.com/paper/2106.06134/) (2021)
- [Finding Global Homophily in Graph Neural Networks When Meeting Heterophily](https://mlsanity.com/paper/2205.07308/) (2022)
- [Scaling Graph Neural Networks with Approximate PageRank](https://mlsanity.com/paper/2007.01570/) (2020)
- [Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking](https://mlsanity.com/paper/2306.10453/) (2023)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers](https://mlsanity.com/paper/2109.02927/) (2021)
- [Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?](https://mlsanity.com/paper/2306.01323/) (2023)
- [BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2106.13092/) (2021)
- [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019)
- [Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election](https://mlsanity.com/paper/1707.00086/) (2017)
- [SATAR: A Self-supervised Approach to Twitter Account Representation Learning and its Application in Bot Detection](https://mlsanity.com/paper/2106.13089/) (2021)
- [BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts](https://mlsanity.com/paper/2304.06280/) (2023)
- [MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark](https://mlsanity.com/paper/2301.01123/) (2023)
- [BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency](https://mlsanity.com/paper/2208.08320/) (2022)

## Built on by (most cited first, 1 of 1)

- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)

## Abstract (arXiv)

The detection of malicious social bots has become a crucial task, as bots can be easily deployed and manipulated to spread disinformation, promote conspiracy messages, and more. Most existing approaches utilize graph neural networks (GNNs)to capture both user profle and structural features,achieving promising progress. However, they still face limitations including the expensive training on large underlying graph, the performance degration when similar neighborhood patterns' assumption preferred by GNNs is not satisfied, and the dynamic features of bots in a highly adversarial context. Motivated by these limitations, this paper proposes a method named BSG4Bot with an intuition that GNNs training on Biased SubGraphs can improve both performance and time/space efficiency in bot detection. Specifically, BSG4Bot first pre-trains a classifier on node features efficiently to define the node similarities, and constructs biased subgraphs by combining the similarities computed by the pre-trained classifier and the node importances computed by Personalized PageRank (PPR scores). BSG4Bot then introduces a heterogeneous GNN over the constructed subgraphs to detect bots effectively and efficiently. The relatively stable features, including the content category and temporal activity features, are explored and incorporated into BSG4Bot after preliminary verification on sample data. The extensive experimental studies show that BSG4Bot outperforms the state-of-the-art bot detection methods, while only needing nearly 1/5 training time.