SqueezeGCN: Adaptive Neighborhood Aggregation with Squeeze Module for Twitter Bot Detection Based on GCN

Abstract

Despite notable advancements in bot detection methods based on Graph Neural Networks (GNNs). The efficacy of Graph Neural Networks relies heavily on the homophily assumption, which posits that nodes with the same label are more likely to form connections between them. However, the latest social bots are capable of concealing themselves by extensively interacting with authentic user accounts, forging extensive connections on social graphs, and thus deviating from the homophily assumption. Consequently, conventional Graph Neural Network methods continue to face significant challenges in detecting these novel types of social bots. To address this issue, we proposed SqueezeGCN, an adaptive neighborhood aggregation with the Squeeze Module for Twitter bot detection based on a GCN. The Squeeze Module uses a parallel multi-layer perceptron (MLP) to squeeze feature vectors into a one-dimensional representation. Subsequently, we adopted the sigmoid activation function, which normalizes values between 0 and 1, serving as node aggregation weights. The aggregation weight vector is processed by a linear layer to obtain the aggregation embedding, and the classification result is generated using a MLP classifier. This design generates adaptive aggregation weights for each node, diverging from the traditional singular neighbor aggregation approach. Our experiments demonstrate that SqueezeGCN performs well on three widely acknowledged Twitter bot detection benchmarks. Comparisons with a GCN reveal improvements of 2.37%, 15.59%, and 1.33% for the respective datasets. Furthermore, our approach demonstrates improvements when compared to state-of-the-art algorithms on the three benchmark datasets. The experimental results further affirm the exceptional effectiveness of our proposed algorithm for Twitter bot detection.