RMFKAN: Network Spammers Detection Method Based on Improved Graph Mamba

Detecting network spammers is crucial for creating a harmonious online environment. Existing graph Transformer-based methods for network spammers detection face challenges due to indiscriminate information propagation between nodes within communities. This leads to overly homogeneous node representations and issues with excessive compression and smoothing when handling long-range dependencies, ultimately reducing the effectiveness of network spammers detection. A novel model, the relational bi-directional graph Mamba Fourier Kolmogorov-Arnold network (RMFKAN), is proposed to address these challenges in detecting network spammers on social platforms. The method of heterogeneous perception long-distance relationship feature extraction is used to solve the problem of feature loss in long-distance relationships across communities in large-scale social networks. The bi-directional selection state space model (Bi-Mamba) is introduced to address the issues of over-compression and over-smoothing when dealing with long-distance dependencies. Specifically, subgraphs are tokenized by the random walk strategy, message passing neural networks are input to independently handle different types of edges, and features are enhanced by KAN improved with Fourier coefficients. The feature matrix is input into Bi-Mamba to improve the ability of capturing long-distance dependencies and effectively reduce training complexity. On the two public online spammer detection datasets Twibot-20 and Twibot-22, compared with 10 baseline models, the experimental results show that RMFKAN is superior to existing baseline methods in multiple evaluation indicators. Compared with the best results of existing research, the F1 score of RMFKAN is increased by 2.10 and 4.06 percentage points respectively, and the accuracy is increased by 1.01 and 4.45 percentage points respectively, which verifies its superior performance in the task of network spammers detection.