BSG4Bot: Efficient Bot Detection based on Biased Heterogeneous Subgraphs

TL;DR

BSG4Bot is a novel bot detection method that constructs biased heterogeneous subgraphs to improve detection accuracy and efficiency, addressing limitations of existing GNN-based approaches in dynamic and large-scale social networks.

Contribution

The paper introduces BSG4Bot, a new approach using biased subgraphs and heterogeneous GNNs to enhance bot detection performance and efficiency.

Findings

Outperforms state-of-the-art methods in accuracy

Requires nearly 1/5 of the training time of existing methods

Effectively incorporates stable features like content and activity patterns

Abstract

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.