Addressing the persistent challenge of learning from imbalanced datasets is crucial in advancing machine learning applications. Standard machine learning algorithms typically assume that the input data is balanced, and they often struggle to effectively learn the distribution of minority class data when dealing with imbalanced data. To address this, our study designed an improved Generative Adversarial Networks (GANs) model, named MDGAN, for tabular sample synthesis to augment samples and balance the data distribution. MDGAN employs a multi-generator and multi-discriminator structure to capture non-connected subspace manifolds, thereby better fitting the complete data distribution. To enhance the diversity among the multiple generators, an exclusive loss among generators was designed, ensuring that each generator produces data of different modalities. Additionally, a contrastive loss was introduced to ensure that the generated samples better fit the minority class distribution and are separated from the majority class distribution, preventing blurred classification boundaries. Qualitative and quantitative tests were conducted on 25 real datasets, and the experimental results indicate that MDGAN outperforms traditional classical models and current advanced oversampling models.

In the domain of complex networks, the identification of influential nodes plays a crucial role in ensuring network stability and facilitating efficient information dissemination . Although the study of influential nodes has been applied in many fields such as suppression of rumor spreading, regulation of group behavior , and prediction of mass events evolution, current deep learning-based algorithms have limited input features and are incapable of aggregating neighbor information of nodes, thus failing to adapt to complex networks. We propose an influential node identification method in complex networks based on the Transformer. In this method, the input sequence of a node includes information about the node itself and its neighbors, enabling the model to effectively aggregate node information to identify its influence. Experiments were conducted on 9 synthetic networks and 12 real networks. Using the SIR model and a benchmark method to verify the effectiveness of our approach. The experimental results show that this method can more effectively identify influential nodes in complex networks. In particular, the method improves 27 percent compared to the second place method in network Netscience and 21 percent in network Faa.