In recent years, significant advancements have been made in contrastive self-supervised learning for graphs. However, most of the existing methods start from the feature level and ignore the structural information. In this work, we propose a graph representation learning based on edge structure contrastive self-supervised framework (SCGRL), which leverages a novel edge-structure-based "masked edges vs. complementary edges" instance pairs to fully utilize the topological information of the graph, and attempts to reconstruct the original graph using the visible graph structure. In the feature processing, normal coded features are constrained with the coded features without gradient updating to enhance the encoder’s prediction ability for the masked representation. In addition, boundary losses are designed to ensure that the model can accurately distinguish between different instance pairs. We conduct extensive experiments on various benchmark datasets to demonstrate that SCGRL outperforms the state-of-the-art in different downstream tasks, especially link prediction.

Time series analysis plays a fundamental role in revealing data evolution, trends, and cyclical patterns. However, existing studies often fail to effectively address the dynamic dependencies between variables in multidimensional time series and the temporal evolution patterns within variables, thereby limiting the effectiveness of complex time series feature analysis. In this paper, we propose a dual-branch frequency-time interactive representation learning model (FreqTime) that captures the correlations between variables and the temporal dependencies within variables through a collaborative architecture in the time domain and frequency domain. The time domain branch uses an inverse Transformer architecture to model cross-variable interactions, while the frequency domain branch utilizes multi-scale gated convolutions to capture features and map them back to the time domain. Finally, global representations are obtained by interactively fusing the representations learned from the two branches in the time domain. Experiments demonstrate that FreqTime achieves state-of-the-art performance on long sequence prediction, classification, and anomaly detection tasks, and exhibits strong robustness in noisy environments.

In credit default prediction, the main challenge is handling complex data structures and addressing data class imbalance . Given class imbalance and multi-dimensional data, general models find it difficult to fully explore the deep interdependencies within the data and the interaction effects between local and global. To overcome these challenges, this study proposes a Weighted Imbalanced Graph Neural Network (WIGNN) model that integrates adaptive graph structure inference with differential weight connectivity strategy, and the model solves the existing problems from the perspective of differential weight connectivity and graph balancing. Here, the weight connection uses the Gaussian kernel function to refine calculations and an adaptive percentile method to adjust sparsity , improving the understanding and efficiency of mining data connections. The weighted graph generated by this method can reflect the interaction between nodes and improve the model’s ability to analyse complex data structures. Based on this weighted graph, the graph imbalance module adopts a reinforcement learning-driven neighbour sampling strategy to adjust the sampling threshold automatically, optimizes the node embedding through message aggregation, and combines with a cost-sensitive matrix to improve classification accuracy and cost-effectiveness of the model on diverse credit datasets. We applied the WIGNN model to six real and class-imbalanced credit datasets, comparing it with 11 mainstream credit default prediction models. Evaluated using metrics Area Under the Curve (AUC), Geometric Mean (G-mean), and Accuracy. The results show that WIGNN significantly outperforms other models in handling class imbalance and graph sparsity , demonstrating its potential in financial credit applications.

Multi-sequence MRI image fusion technology aids radiologists in quickly and accurately assessing ischemic lesions and their surrounding areas by combining DWI and FLAIR images to generate information-rich fusion images. Despite the rapid development of medical image fusion techniques, existing methods are predominantly focused on technical-level model optimization and fail to effectively integrate medical domain knowledge. This limitation reduces their clinical applicability and model interpretability. Inspired by radiologists' diagnostic pattern, which involves focusing on and enlarging lesion areas, we propose a medical domain knowledge-inspired area amplification network for multi-sequence MRI image fusion in ischemic stroke, named MDKFusion. Specifically, we design the Lesion Area Amplification (LAA) module, which uses bicubic interpolation for adaptive amplification and incorporates crosslevel and neighboring-level feature mapping with high-level feature co-guidance. This design emulates radiologists' practice of zooming in to examine lesions, thereby enhancing interpretability. Additionally, we employ the Feature Guidance Module (FGM) to achieve progressive guidance and feature integration. We further introduce the ℒSCDloss function to minimize pixel discrepancies between source and fused images, improving fusion quality. Compared to various mainstream fusion methods, MDKFusion achieves state-of-the-art (SOTA) performance across eight objective evaluation metrics. To confirm its practical value in clinical diagnosis, we invited five radiologists to perform a subjective evaluation of the fused images. Our code will be available at https://github.com/MinLila/MDKFusion.

Autoencoders, as a generative self-supervised learning, have received more and more attention in recent years in image, video, and other media-related information processing. However, Graph AutoEncoder (GAE) has yet to achieve the capability demonstrated by contrastive learning in the task-centered on attribute networks. The main limitation lies in the fact that traditional autoencoder architectures require pretext tasks that align with downstream tasks, resulting in limited expressive power of the encoder. In this paper, we propose a novel separable-task generative self-supervised learning framework capable of providing high-quality representations, Split Masked AutoEncoder (SMAE), which unleashes the encoder’s ability to extract representations through an intelligent design. Our approach focuses on unlocking the potential of the encoder by introducing encoding transfer and feature replacement strategies, thereby enabling self-supervised pretext tasks to achieve atomic separation and fully unleash the encoder’s feature representation potential. We conducted extensive experiments on widely-used graph classification datasets, and the results demonstrate that SMAE outperforms state-of-the-art baselines in terms of graph classification accuracy and generation quality. Furthermore, our experimental findings show that prediction at the representation layer is more effective than original graph layer reconstruction in the field of masked graph autoencoders.

Structural anomalies are the core problem in graph anomaly detection. However, the current mainstream self-supervised graph anomaly detection models do not directly model structural anomalies and their expensive time consumption limits the efficiency of graph anomaly detection. For this reason, we rethink graph anomaly detection and propose a self-supervised Group Discrimination paradigm with Structure-Aware (GDSA). Our model can be explicitly aware of the graph topology changes by multi-view structure disturbance. Moreover, GDSA transforms graph anomaly detection into discriminating the scalar summaries of positive and negative group nodes. The results of extensive experiments on four benchmark datasets show that GDSA outperforms current state-of-the-art methods, with the most significant AUC performance improvement of 28.7%. Notably, in scalability testing on a large-scale dataset, the training time and testing time of GDSA are 1181.0× and 5064.7× faster than the baseline, respectively, with 61.9% savings in memory usage.

Time series aomaly detection has been widely studied in recent years. Previous research focuses on point-wise features and pairwise associations for feature learning or designed anomaly scores based on prior knowledge. However, these methods cannot fully learn the intricate abnormal dynamic information and can only identify a limited class of anomalies. We propose a Masked Attention Network with Query Sparsity Measurement (MAN-QSM) to address the above challenges. This model uses two kinds of prior knowledge to fully exploit the differences between normal and abnormal points from two perspectives: pairwise association and sequence-level information. We designs the anomaly mask mechanism to collaborate with the training strategy to amplify the difference between normal and abnormal points. In experiments, we compare the model with classical methods, reconstruction-based models, autoregressive-based models, and state-of-the-art models, and the MAN-QSM achieves state-of-the-art results on SMD, PSM, and MSL datasets with an average of 16% reduction in error rate.